Data Mining Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Mining and Text Mining - Project\n",
    "In This notebook we will explain the main steps that brought us to our finalmodel "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check if there is some trend in the sales considering all days of the week. In the first part we focus on all the store and we will make a further analysis on regional and store type trends\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10da05710>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import dataset.dataset as d\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = d.read_dataset()\n",
    "df['Date'] = df['Date'].astype(\"datetime64[s]\")\n",
    "df['day_of_week'] = df['Date'].dt.weekday_name\n",
    "df_sales_per_day = df.groupby(df[\"day_of_week\"])['NumberOfSales'].agg('sum')\n",
    "\n",
    "df_sales_per_day.plot(kind=\"bar\", title='Sales divided by Day of the Week' )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e646710>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = d.read_dataset()\n",
    "df['Date'] = df['Date'].astype(\"datetime64[s]\")\n",
    "df['day_of_week'] = df['Date'].dt.weekday_name\n",
    "df_divided_by_month = df.groupby(df[\"Date\"].dt.month)['NumberOfSales'].agg('sum')\n",
    "df_divided_by_month.plot(kind=\"bar\", title='Sales divided by Month' )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}