Lilith_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qofDNHo6BNQu",
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-47fb62c0cb045bbe",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "# Project GEVPRO (H&M) - MAIN\n",
    "\n",
    "We will work with the following dataset: \n",
    "Source: https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/overview\n",
    "This dataset has been reduced to use less memory. Only sales data from > 08-01-2020 is selected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JYg2fp2UBNQy",
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-27121c498ff67f14",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "f0b8f49e-c01f-4a37-e212-d2995a088d2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1356119, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Used libaries:\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "df = pd.read_csv('data/customers.csv', low_memory=False)\n",
    "df = df.dropna(subset=['age', 'postal_code'])  # drop rows with missing values in this column\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.  Research questions:\n",
    "We try to answer the following research questions:\n",
    "1. \n",
    "2. \n",
    "3. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.  Reducing the dataset: \n",
    "This is only ran on the default dataset (transactions_train.csv) found on kaggle to reduce the memory size to 91.3mb (instead of 3+gb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fE-wygVlBNQ0",
    "outputId": "e70dbf09-5aac-4d67-b6e3-8ee41d7a1730"
   },
   "outputs": [],
   "source": [
    "# df = pd.read_csv('data/transactions_train.csv', low_memory=False)\n",
    "# df['t_dat'] = pd.to_datetime(df['t_dat'])\n",
    "# mask = df['t_dat'] > '08-01-2020'\n",
    "# df_reduced = df.loc[mask]\n",
    "# df_reduced.to_csv('C:\\\\Users\\\\Nils\\\\Jupyter Notebooks\\\\Project_gevpro\\\\transactions_reduced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "tt1B9_GsBNQ1",
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-7382b3537acda224",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "outputId": "808ba787-9d05-4f92-ad3f-a2f8416fa813"
   },
   "outputs": [],
   "source": [
    "# This loads the reduced transaction (sales) dataset.\n",
    "df = pd.read_csv('data/transactions_reduced.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/customers.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the most frequent age of H&M customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0    67530\n",
       "24.0    56124\n",
       "20.0    55196\n",
       "25.0    54989\n",
       "23.0    54867\n",
       "26.0    53658\n",
       "22.0    51869\n",
       "27.0    49134\n",
       "28.0    44294\n",
       "29.0    40697\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What are the 10 most frequent ages?\n",
    "df[\"age\"].value_counts().head(10)\n",
    "\n",
    "#The most frequent age is 21, and the most people shopping at H&M are between 20 and 30\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of the ages of H&M customers? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is the distribution of age?\n",
    "sns.set_theme(style='darkgrid')\n",
    "ax = sns.histplot(x=\"age\", data=df, color='green', bins=40)\n",
    "\n",
    "#the distribution of age centers around 20/30 and 45/55."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the most frequent postal code? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2c29ae653a9282cce4151bd87643c907644e09541abc28ae87dea0d1f6603b1c    120303\n",
       "cc4ed85e30f4977dae47662ddc468cd2eec11472de6fac5ec985080fd92243c8       261\n",
       "714976379549eb90aae4a71bca6c7402cc646ae7c40f6c1cb91d4b5a18623fc1       159\n",
       "7c1fa3b0ec1d37ce2c3f34f63bd792f3b4494f324b6be5d1e4ba6a75456b96a7       157\n",
       "5b7eb31eabebd3277de632b82267286d847fd5d44287ee150bb4206b48439145       156\n",
       "1f5bd429acc88fbbf24de844a59e438704aa8761bc7b99fd977cad297c50b74c       154\n",
       "9d5787501bf1c77592156ba51eab13f4a2670c807686431a9e22a69090b02358       141\n",
       "a1959a16bf167858c93a66ec2a330644512b25fb10f97eee2058549885af4dbd       138\n",
       "087a46b65170845b4a55226ff1eb748ce7843d4b637cbe17f6bfbd1e645d2ffb       134\n",
       "4d22901a97bd952bb92cdd31b38c39b41dd3250a735cabdfac1503eb405b807d       132\n",
       "Name: postal_code, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what are the 10 most frequent postal codes\n",
    "df['postal_code'].value_counts().head(10)\n",
    "\n",
    "#the most frequent postal code appears to be a value that gives 120303"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many customers have a club membership?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#how many customers have a club_membership?\n",
    "sns.set_theme(style='darkgrid')\n",
    "ax = sns.histplot(x=\"club_member_status\", data=df, color='green', bins=40)\n",
    "\n",
    "#The biggest part of the customers have a club membership"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What kind of fashion news frequency is most popular with what club membership?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#uniting the values that all mean none but have different named values for this \n",
    "df.loc[~df['fashion_news_frequency'].isin(['Regularly', 'Monthly']), 'fashion_news_frequency'] = 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['None', 'Regularly', 'Monthly'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#showing the new values for fashion news frequency\n",
    "df['fashion_news_frequency'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>fashion_news_frequency</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>None</th>\n",
       "      <th>Regularly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>club_member_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACTIVE</th>\n",
       "      <td>778</td>\n",
       "      <td>800409</td>\n",
       "      <td>471304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEFT CLUB</th>\n",
       "      <td>0</td>\n",
       "      <td>459</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRE-CREATE</th>\n",
       "      <td>59</td>\n",
       "      <td>87114</td>\n",
       "      <td>5787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fashion_news_frequency  Monthly    None  Regularly\n",
       "club_member_status                                \n",
       "ACTIVE                      778  800409     471304\n",
       "LEFT CLUB                     0     459          8\n",
       "PRE-CREATE                   59   87114       5787"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the club_member_status against the fashion news frequency\n",
    "pd.crosstab(df['club_member_status'], df['fashion_news_frequency'])\n",
    "\n",
    "#There are no people that leave the club that have monthly news updates. \n",
    "#The people that have no news updates, which are the most people that leave the club, dont receive fashion news. \n",
    "#The most people have an active status but do not receive news updates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many people receive fashion news? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# division of fashion_news_frequency\n",
    "sns.set_theme(style='darkgrid')\n",
    "ax = sns.histplot(x=\"fashion_news_frequency\", data=df, color='green', bins=40)\n",
    "\n",
    "#most people don't like to receive fashion news"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the spread of fashion news in contrast to club member status?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A visual representation of the fashion news frequency and the club member status\n",
    "fashion_status = sns.countplot(y='fashion_news_frequency', hue='club_member_status', data=df, palette=\"Set3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the relation between receiving fashion news and the customers age?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>age</th>\n",
       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>19.0</th>\n",
       "      <th>20.0</th>\n",
       "      <th>21.0</th>\n",
       "      <th>22.0</th>\n",
       "      <th>23.0</th>\n",
       "      <th>24.0</th>\n",
       "      <th>25.0</th>\n",
       "      <th>...</th>\n",
       "      <th>91.0</th>\n",
       "      <th>92.0</th>\n",
       "      <th>93.0</th>\n",
       "      <th>94.0</th>\n",
       "      <th>95.0</th>\n",
       "      <th>96.0</th>\n",
       "      <th>97.0</th>\n",
       "      <th>98.0</th>\n",
       "      <th>99.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fashion_news_frequency</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monthly</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>31</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>841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>86</td>\n",
       "      <td>6405</td>\n",
       "      <td>16216</td>\n",
       "      <td>22646</td>\n",
       "      <td>33292</td>\n",
       "      <td>40891</td>\n",
       "      <td>31905</td>\n",
       "      <td>35318</td>\n",
       "      <td>37744</td>\n",
       "      <td>37778</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>879067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regularly</th>\n",
       "      <td>75</td>\n",
       "      <td>2987</td>\n",
       "      <td>8107</td>\n",
       "      <td>15055</td>\n",
       "      <td>21894</td>\n",
       "      <td>26623</td>\n",
       "      <td>19948</td>\n",
       "      <td>19523</td>\n",
       "      <td>18358</td>\n",
       "      <td>17180</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>476211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>161</td>\n",
       "      <td>9392</td>\n",
       "      <td>24323</td>\n",
       "      <td>37707</td>\n",
       "      <td>55196</td>\n",
       "      <td>67530</td>\n",
       "      <td>51869</td>\n",
       "      <td>54867</td>\n",
       "      <td>56124</td>\n",
       "      <td>54989</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>1356119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "age                     16.0  17.0   18.0   19.0   20.0   21.0   22.0   23.0  \\\n",
       "fashion_news_frequency                                                         \n",
       "Monthly                    0     0      0      6     10     16     16     26   \n",
       "None                      86  6405  16216  22646  33292  40891  31905  35318   \n",
       "Regularly                 75  2987   8107  15055  21894  26623  19948  19523   \n",
       "All                      161  9392  24323  37707  55196  67530  51869  54867   \n",
       "\n",
       "age                      24.0   25.0  ...  91.0  92.0  93.0  94.0  95.0  96.0  \\\n",
       "fashion_news_frequency                ...                                       \n",
       "Monthly                    22     31  ...     0     0     0     0     0     0   \n",
       "None                    37744  37778  ...    12    10     4     2     8     3   \n",
       "Regularly               18358  17180  ...     4     7     0     1     2     0   \n",
       "All                     56124  54989  ...    16    17     4     3    10     3   \n",
       "\n",
       "age                     97.0  98.0  99.0      All  \n",
       "fashion_news_frequency                             \n",
       "Monthly                    0     0     0      841  \n",
       "None                       3     5     8   879067  \n",
       "Regularly                  1     2     5   476211  \n",
       "All                        4     7    13  1356119  \n",
       "\n",
       "[4 rows x 85 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fashion news in relation to age \n",
    "pd.crosstab(df['fashion_news_frequency'], df['age'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x141f7c5b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1221.42x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fashion news frequency in relation to age \n",
    "sns.displot(data=df, x=\"age\", hue=\"fashion_news_frequency\", col=\"fashion_news_frequency\")\n",
    "\n",
    "#the highest amount of customers don't receive fashion news\n",
    "#There are almost no people that receive fashion news monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "README file submission for customers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the customer section of this project we have explored the following research questions:\n",
    "What is the most frequent age of H&M customers?\n",
    "What is the distribution of the ages of H&M customers? \n",
    "What is the most frequent postal code? \n",
    "How many customers have a club membership?\n",
    "What kind of fashion news frequency is most popular with what club membership?\n",
    "How many people receive fashion news? \n",
    "What is the spread of fashion news in contrast to club member status?\n",
    "What is the relation between receiving fashion news and the customers age?\n",
    "\n",
    "We have expressed the data in different ways, varying from plots to tables. With this research, we try to create a better overview of the customers shopping at H&M.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "assignment3.ipynb",
   "provenance": []
  },
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}