RH_PA3drafts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7aed3def-4f6e-4b77-91f5-65791c4ed7ea",
   "metadata": {},
   "source": [
    "# INFOB2DA PA3 | Rijk's drafts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "4f691a0f-da46-40a2-8b7b-fd240a50e2b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing libraries succesful\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.metrics import davies_bouldin_score, calinski_harabasz_score\n",
    "import numpy as np\n",
    "print(\"Importing libraries succesful\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "193a9760-1686-401a-a362-f54fc62725d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Feb</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <td>Returning_Visitor</td>\n",
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       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
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       "      <td>2.666667</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>154.216667</td>\n",
       "      <td>0.015789</td>\n",
       "      <td>0.024561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\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",
       "5               0                      0.0              0   \n",
       "6               0                      0.0              0   \n",
       "7               1                      0.0              0   \n",
       "8               0                      0.0              0   \n",
       "9               0                      0.0              0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0               1                 0.000000   \n",
       "1                     0.0               2                64.000000   \n",
       "2                     0.0               1                 0.000000   \n",
       "3                     0.0               2                 2.666667   \n",
       "4                     0.0              10               627.500000   \n",
       "5                     0.0              19               154.216667   \n",
       "6                     0.0               1                 0.000000   \n",
       "7                     0.0               0                 0.000000   \n",
       "8                     0.0               2                37.000000   \n",
       "9                     0.0               3               738.000000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0     0.200000   0.200000         0.0         0.0   Feb                 1   \n",
       "1     0.000000   0.100000         0.0         0.0   Feb                 2   \n",
       "2     0.200000   0.200000         0.0         0.0   Feb                 4   \n",
       "3     0.050000   0.140000         0.0         0.0   Feb                 3   \n",
       "4     0.020000   0.050000         0.0         0.0   Feb                 3   \n",
       "5     0.015789   0.024561         0.0         0.0   Feb                 2   \n",
       "6     0.200000   0.200000         0.0         0.4   Feb                 2   \n",
       "7     0.200000   0.200000         0.0         0.0   Feb                 1   \n",
       "8     0.000000   0.100000         0.0         0.8   Feb                 2   \n",
       "9     0.000000   0.022222         0.0         0.4   Feb                 2   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  \n",
       "3        2       2            4  Returning_Visitor    False    False  \n",
       "4        3       1            4  Returning_Visitor     True    False  \n",
       "5        2       1            3  Returning_Visitor    False    False  \n",
       "6        4       3            3  Returning_Visitor    False    False  \n",
       "7        2       1            5  Returning_Visitor     True    False  \n",
       "8        2       2            3  Returning_Visitor    False    False  \n",
       "9        4       1            2  Returning_Visitor    False    False  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"online_shoppers_intention 1.csv\")\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921617bd-f353-417e-a6b5-a1bdc8b2df09",
   "metadata": {},
   "source": [
    "We are comparing the Operating System 13 with the rest of the customers. First, we select the columns that best define \n",
    "1 A customer's behavior on certain pages\n",
    "2 A customer's behavior switching pager\n",
    "3 If a customer generates revenue\n",
    "\n",
    "We will apply fitting summary statistics and visualize the results in a way that makes comparison easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a8328276-fa6d-4b33-a576-fedaddda4841",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5679</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>225.766667</td>\n",
       "      <td>9630.209524</td>\n",
       "      <td>0.053355</td>\n",
       "      <td>0.066159</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8003</th>\n",
       "      <td>77.333333</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1051.291667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014815</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8006</th>\n",
       "      <td>446.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>815.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>8115</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>8187</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>129.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>8299</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8328</th>\n",
       "      <td>60.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>True</td>\n",
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       "      <th>8368</th>\n",
       "      <td>1652.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.005882</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8467</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8635</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>784.883333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Administrative_Duration  Informational_Duration  \\\n",
       "5679                 0.000000              225.766667   \n",
       "8003                77.333333                7.000000   \n",
       "8006               446.250000                0.000000   \n",
       "8115                 0.000000                0.000000   \n",
       "8187                 0.000000                0.000000   \n",
       "8299                 0.000000                0.000000   \n",
       "8328                60.500000                0.000000   \n",
       "8368              1652.000000                0.000000   \n",
       "8467                61.000000               31.000000   \n",
       "8635                 0.000000                0.000000   \n",
       "\n",
       "      ProductRelated_Duration  BounceRates  ExitRates  Revenue  \n",
       "5679              9630.209524     0.053355   0.066159    False  \n",
       "8003              1051.291667     0.000000   0.014815    False  \n",
       "8006               815.250000     0.000000   0.002500    False  \n",
       "8115                87.000000     0.000000   0.028571    False  \n",
       "8187               129.500000     0.000000   0.050000    False  \n",
       "8299               222.000000     0.000000   0.040000     True  \n",
       "8328                50.000000     0.000000   0.025000     True  \n",
       "8368               446.375000     0.000000   0.005882    False  \n",
       "8467                43.000000     0.000000   0.025000    False  \n",
       "8635               784.883333     0.000000   0.004762     True  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties = [\"Administrative_Duration\", \"Informational_Duration\", \"ProductRelated_Duration\",\n",
    "              \"BounceRates\", \"ExitRates\", \"Revenue\"]\n",
    "\n",
    "df_13 = df[properties][df[\"Browser\"] == 13]\n",
    "df_13.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fb9c8b8e-9268-4a82-9c66-80d786ee09f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe above has\n",
      "\n",
      "61 rows\n",
      "6 columns\n"
     ]
    }
   ],
   "source": [
    "rows = df_13.shape[0]\n",
    "columns = df_13.shape[1]\n",
    "\n",
    "print(f\"The dataframe above has\\n\\n{rows} rows\\n{columns} columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6d24423c-4d2f-4975-9ec8-aa7cc38d02d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational_Duration</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>False</td>\n",
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       "      <th>8635</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>784.883333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>True</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "      Administrative_Duration  Informational_Duration  \\\n",
       "5679                      NaN              225.766667   \n",
       "8003                77.333333                7.000000   \n",
       "8006               446.250000                     NaN   \n",
       "8115                      NaN                     NaN   \n",
       "8187                      NaN                     NaN   \n",
       "8299                      NaN                     NaN   \n",
       "8328                60.500000                     NaN   \n",
       "8368              1652.000000                     NaN   \n",
       "8467                61.000000               31.000000   \n",
       "8635                      NaN                     NaN   \n",
       "\n",
       "      ProductRelated_Duration  BounceRates  ExitRates  Revenue  \n",
       "5679              9630.209524     0.053355   0.066159    False  \n",
       "8003              1051.291667          NaN   0.014815    False  \n",
       "8006               815.250000          NaN   0.002500    False  \n",
       "8115                87.000000          NaN   0.028571    False  \n",
       "8187               129.500000          NaN   0.050000    False  \n",
       "8299               222.000000          NaN   0.040000     True  \n",
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       "8368               446.375000          NaN   0.005882    False  \n",
       "8467                43.000000          NaN   0.025000    False  \n",
       "8635               784.883333          NaN   0.004762     True  "
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     "execution_count": 71,
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBI score: 0.761093645569819\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(df[df[\"PageValues\"] <= 0].index)\n",
    "df[\"normalizedPageValues\"] = (df[\"PageValues\"] - df[\"PageValues\"].min()) / (df[\"PageValues\"].max() - df[\"PageValues\"].min())\n",
    "\n",
    "X = df[[\"normalizedPageValues\", \"ExitRates\"]]\n",
    "\n",
    "db = DBSCAN(eps=0.01, min_samples=6)\n",
    "labels = db.fit_predict(X)\n",
    "\n",
    "labels = labels.astype(str)\n",
    "fig = px.scatter(df, x=\"normalizedPageValues\", y=\"ExitRates\", color=labels, \n",
    "                 color_discrete_map={\"0\": \"purple\", \"-1\": \"orange\"}\n",
    "                )\n",
    "fig.show()\n",
    "\n",
    "dbi = davies_bouldin_score(X, labels)\n",
    "print(f\"DBI score: {dbi}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "301df59b-83ee-4bed-af0a-e1c1d346da02",
   "metadata": {},
   "outputs": [],
   "source": []
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