Data_Cleaning_and_Preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9c35135",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f251eb49",
   "metadata": {},
   "source": [
    "# Load CSV data into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cc3a685",
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>64.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</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>2</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>2.666667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</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",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</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",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "\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",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
       "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
       "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
       "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
       "4         0.02       0.05         0.0         0.0   Feb                 3   \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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"Resources/online_shoppers_intention.csv\"\n",
    "shoppers_df = pd.read_csv(file_path)\n",
    "shoppers_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b84ba7",
   "metadata": {},
   "source": [
    "# 1 Checking for Null values in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c998a019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Administrative             0\n",
       "Administrative_Duration    0\n",
       "Informational              0\n",
       "Informational_Duration     0\n",
       "ProductRelated             0\n",
       "ProductRelated_Duration    0\n",
       "BounceRates                0\n",
       "ExitRates                  0\n",
       "PageValues                 0\n",
       "SpecialDay                 0\n",
       "Month                      0\n",
       "OperatingSystems           0\n",
       "Browser                    0\n",
       "Region                     0\n",
       "TrafficType                0\n",
       "VisitorType                0\n",
       "Weekend                    0\n",
       "Revenue                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shoppers_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b3e829",
   "metadata": {},
   "source": [
    "### There are No Null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794af70d",
   "metadata": {},
   "source": [
    "# 2 Check type of data per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b1b4119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Administrative           12330 non-null  int64  \n",
      " 1   Administrative_Duration  12330 non-null  float64\n",
      " 2   Informational            12330 non-null  int64  \n",
      " 3   Informational_Duration   12330 non-null  float64\n",
      " 4   ProductRelated           12330 non-null  int64  \n",
      " 5   ProductRelated_Duration  12330 non-null  float64\n",
      " 6   BounceRates              12330 non-null  float64\n",
      " 7   ExitRates                12330 non-null  float64\n",
      " 8   PageValues               12330 non-null  float64\n",
      " 9   SpecialDay               12330 non-null  float64\n",
      " 10  Month                    12330 non-null  object \n",
      " 11  OperatingSystems         12330 non-null  int64  \n",
      " 12  Browser                  12330 non-null  int64  \n",
      " 13  Region                   12330 non-null  int64  \n",
      " 14  TrafficType              12330 non-null  int64  \n",
      " 15  VisitorType              12330 non-null  object \n",
      " 16  Weekend                  12330 non-null  bool   \n",
      " 17  Revenue                  12330 non-null  bool   \n",
      "dtypes: bool(2), float64(7), int64(7), object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "shoppers_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2d4d7d",
   "metadata": {},
   "source": [
    "### All the rows have the same number of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "240c9f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.315166</td>\n",
       "      <td>80.818611</td>\n",
       "      <td>0.503569</td>\n",
       "      <td>34.472398</td>\n",
       "      <td>31.731468</td>\n",
       "      <td>1194.746220</td>\n",
       "      <td>0.022191</td>\n",
       "      <td>0.043073</td>\n",
       "      <td>5.889258</td>\n",
       "      <td>0.061427</td>\n",
       "      <td>2.124006</td>\n",
       "      <td>2.357097</td>\n",
       "      <td>3.147364</td>\n",
       "      <td>4.069586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.321784</td>\n",
       "      <td>176.779107</td>\n",
       "      <td>1.270156</td>\n",
       "      <td>140.749294</td>\n",
       "      <td>44.475503</td>\n",
       "      <td>1913.669288</td>\n",
       "      <td>0.048488</td>\n",
       "      <td>0.048597</td>\n",
       "      <td>18.568437</td>\n",
       "      <td>0.198917</td>\n",
       "      <td>0.911325</td>\n",
       "      <td>1.717277</td>\n",
       "      <td>2.401591</td>\n",
       "      <td>4.025169</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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>7.000000</td>\n",
       "      <td>184.137500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>598.936905</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>0.025156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.256250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1464.157214</td>\n",
       "      <td>0.016813</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>3398.750000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2549.375000</td>\n",
       "      <td>705.000000</td>\n",
       "      <td>63973.522230</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>361.763742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Administrative  Administrative_Duration  Informational  \\\n",
       "count    12330.000000             12330.000000   12330.000000   \n",
       "mean         2.315166                80.818611       0.503569   \n",
       "std          3.321784               176.779107       1.270156   \n",
       "min          0.000000                 0.000000       0.000000   \n",
       "25%          0.000000                 0.000000       0.000000   \n",
       "50%          1.000000                 7.500000       0.000000   \n",
       "75%          4.000000                93.256250       0.000000   \n",
       "max         27.000000              3398.750000      24.000000   \n",
       "\n",
       "       Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "count            12330.000000    12330.000000             12330.000000   \n",
       "mean                34.472398       31.731468              1194.746220   \n",
       "std                140.749294       44.475503              1913.669288   \n",
       "min                  0.000000        0.000000                 0.000000   \n",
       "25%                  0.000000        7.000000               184.137500   \n",
       "50%                  0.000000       18.000000               598.936905   \n",
       "75%                  0.000000       38.000000              1464.157214   \n",
       "max               2549.375000      705.000000             63973.522230   \n",
       "\n",
       "        BounceRates     ExitRates    PageValues    SpecialDay  \\\n",
       "count  12330.000000  12330.000000  12330.000000  12330.000000   \n",
       "mean       0.022191      0.043073      5.889258      0.061427   \n",
       "std        0.048488      0.048597     18.568437      0.198917   \n",
       "min        0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.014286      0.000000      0.000000   \n",
       "50%        0.003112      0.025156      0.000000      0.000000   \n",
       "75%        0.016813      0.050000      0.000000      0.000000   \n",
       "max        0.200000      0.200000    361.763742      1.000000   \n",
       "\n",
       "       OperatingSystems       Browser        Region   TrafficType  \n",
       "count      12330.000000  12330.000000  12330.000000  12330.000000  \n",
       "mean           2.124006      2.357097      3.147364      4.069586  \n",
       "std            0.911325      1.717277      2.401591      4.025169  \n",
       "min            1.000000      1.000000      1.000000      1.000000  \n",
       "25%            2.000000      2.000000      1.000000      2.000000  \n",
       "50%            2.000000      2.000000      3.000000      2.000000  \n",
       "75%            3.000000      2.000000      4.000000      4.000000  \n",
       "max            8.000000     13.000000      9.000000     20.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shoppers_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd19cb5c",
   "metadata": {},
   "source": [
    "# 3 Check for data imbalance\n",
    "### We will use the column 'Revenue' as it contains the desired output of the customer's activity.\n",
    "### A value of 'True' means that the visitor made a purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6876d9be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(5,5))\n",
    "g = sns.countplot(x='Revenue', data=shoppers_df, ax=axs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8555e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10422\n",
       "True      1908\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shoppers_df['Revenue'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d7a873",
   "metadata": {},
   "source": [
    "### The data is imbalanced.\n",
    "\n",
    "### 81.7% = False (did not make a purchase), and 18.3% = True (made a purchase)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f798161a",
   "metadata": {},
   "source": [
    "# 4 The Data is clean and needs no modification to be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdc4ce2c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}