Project 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import RidgeClassifier, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, r2_score, classification_report\n",
    "from sklearn.model_selection import cross_val_score, KFold, ShuffleSplit\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "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></th>\n",
       "      <th>TransactionNo</th>\n",
       "      <th>Date</th>\n",
       "      <th>ProductNo</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>CustomerNo</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581482</td>\n",
       "      <td>12/9/2019</td>\n",
       "      <td>22485</td>\n",
       "      <td>Set Of 2 Wooden Market Crates</td>\n",
       "      <td>21.47</td>\n",
       "      <td>12</td>\n",
       "      <td>17490.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>581475</td>\n",
       "      <td>12/9/2019</td>\n",
       "      <td>22596</td>\n",
       "      <td>Christmas Star Wish List Chalkboard</td>\n",
       "      <td>10.65</td>\n",
       "      <td>36</td>\n",
       "      <td>13069.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>581475</td>\n",
       "      <td>12/9/2019</td>\n",
       "      <td>23235</td>\n",
       "      <td>Storage Tin Vintage Leaf</td>\n",
       "      <td>11.53</td>\n",
       "      <td>12</td>\n",
       "      <td>13069.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>581475</td>\n",
       "      <td>12/9/2019</td>\n",
       "      <td>23272</td>\n",
       "      <td>Tree T-Light Holder Willie Winkie</td>\n",
       "      <td>10.65</td>\n",
       "      <td>12</td>\n",
       "      <td>13069.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>581475</td>\n",
       "      <td>12/9/2019</td>\n",
       "      <td>23239</td>\n",
       "      <td>Set Of 4 Knick Knack Tins Poppies</td>\n",
       "      <td>11.94</td>\n",
       "      <td>6</td>\n",
       "      <td>13069.0</td>\n",
       "      <td>United Kingdom</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536345</th>\n",
       "      <td>C536548</td>\n",
       "      <td>12/1/2018</td>\n",
       "      <td>22168</td>\n",
       "      <td>Organiser Wood Antique White</td>\n",
       "      <td>18.96</td>\n",
       "      <td>-2</td>\n",
       "      <td>12472.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536346</th>\n",
       "      <td>C536548</td>\n",
       "      <td>12/1/2018</td>\n",
       "      <td>21218</td>\n",
       "      <td>Red Spotty Biscuit Tin</td>\n",
       "      <td>14.09</td>\n",
       "      <td>-3</td>\n",
       "      <td>12472.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536347</th>\n",
       "      <td>C536548</td>\n",
       "      <td>12/1/2018</td>\n",
       "      <td>20957</td>\n",
       "      <td>Porcelain Hanging Bell Small</td>\n",
       "      <td>11.74</td>\n",
       "      <td>-1</td>\n",
       "      <td>12472.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536348</th>\n",
       "      <td>C536548</td>\n",
       "      <td>12/1/2018</td>\n",
       "      <td>22580</td>\n",
       "      <td>Advent Calendar Gingham Sack</td>\n",
       "      <td>16.35</td>\n",
       "      <td>-4</td>\n",
       "      <td>12472.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536349</th>\n",
       "      <td>C536548</td>\n",
       "      <td>12/1/2018</td>\n",
       "      <td>22767</td>\n",
       "      <td>Triple Photo Frame Cornice</td>\n",
       "      <td>20.45</td>\n",
       "      <td>-2</td>\n",
       "      <td>12472.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536350 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TransactionNo       Date ProductNo  \\\n",
       "0             581482  12/9/2019     22485   \n",
       "1             581475  12/9/2019     22596   \n",
       "2             581475  12/9/2019     23235   \n",
       "3             581475  12/9/2019     23272   \n",
       "4             581475  12/9/2019     23239   \n",
       "...              ...        ...       ...   \n",
       "536345       C536548  12/1/2018     22168   \n",
       "536346       C536548  12/1/2018     21218   \n",
       "536347       C536548  12/1/2018     20957   \n",
       "536348       C536548  12/1/2018     22580   \n",
       "536349       C536548  12/1/2018     22767   \n",
       "\n",
       "                                ProductName  Price  Quantity  CustomerNo  \\\n",
       "0             Set Of 2 Wooden Market Crates  21.47        12     17490.0   \n",
       "1       Christmas Star Wish List Chalkboard  10.65        36     13069.0   \n",
       "2                  Storage Tin Vintage Leaf  11.53        12     13069.0   \n",
       "3         Tree T-Light Holder Willie Winkie  10.65        12     13069.0   \n",
       "4         Set Of 4 Knick Knack Tins Poppies  11.94         6     13069.0   \n",
       "...                                     ...    ...       ...         ...   \n",
       "536345         Organiser Wood Antique White  18.96        -2     12472.0   \n",
       "536346               Red Spotty Biscuit Tin  14.09        -3     12472.0   \n",
       "536347         Porcelain Hanging Bell Small  11.74        -1     12472.0   \n",
       "536348         Advent Calendar Gingham Sack  16.35        -4     12472.0   \n",
       "536349           Triple Photo Frame Cornice  20.45        -2     12472.0   \n",
       "\n",
       "               Country  \n",
       "0       United Kingdom  \n",
       "1       United Kingdom  \n",
       "2       United Kingdom  \n",
       "3       United Kingdom  \n",
       "4       United Kingdom  \n",
       "...                ...  \n",
       "536345         Germany  \n",
       "536346         Germany  \n",
       "536347         Germany  \n",
       "536348         Germany  \n",
       "536349         Germany  \n",
       "\n",
       "[536350 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Sales Transaction v.4a.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransactionNo     object\n",
       "Date              object\n",
       "ProductNo         object\n",
       "ProductName       object\n",
       "Price            float64\n",
       "Quantity           int64\n",
       "CustomerNo       float64\n",
       "Country           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransactionNo     0\n",
       "Date              0\n",
       "ProductNo         0\n",
       "ProductName       0\n",
       "Price             0\n",
       "Quantity          0\n",
       "CustomerNo       55\n",
       "Country           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CustomerNo'] = df.CustomerNo.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransactionNo    0\n",
       "Date             0\n",
       "ProductNo        0\n",
       "ProductName      0\n",
       "Price            0\n",
       "Quantity         0\n",
       "CustomerNo       0\n",
       "Country          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ProductNo'] = pd.to_numeric(df['ProductNo'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerNo\n",
       "17841.0    7967\n",
       "14911.0    5800\n",
       "14096.0    5093\n",
       "12748.0    4627\n",
       "14606.0    2773\n",
       "           ... \n",
       "17948.0       1\n",
       "12081.0       1\n",
       "13775.0       1\n",
       "13017.0       1\n",
       "17558.0       1\n",
       "Name: count, Length: 4739, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.CustomerNo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Price.value_counts().plot(kind= 'hist', color= 'green')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Price', 'Quantity', 'CustomerNo']]\n",
    "y = df['ProductName']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: \n",
    "\n",
    "Gabriel Ramos. May-2022. E-commerce Business Transaction, version 4a. Retrieved 09/05/2023 from https://www.kaggle.com/datasets/gabrielramos87/an-online-shop-business-"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}