OrderForecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9725830823952056\n",
      "[ 0.3768652  28.49689061 26.18560134 -6.1459903 ]\n",
      "train set\n",
      "2.9069575412465705\n",
      "16.425978913069915\n",
      "4.052897594693198\n",
      "test set\n",
      "3.7399003630520937\n",
      "26.513471226252285\n",
      "5.149123345410584\n",
      "all data model\n",
      "0.9657592890687285\n",
      "[ 0.3928302  27.30893127 27.31343961 -5.4269271 ]\n",
      "   Days Of launch  stage  outlier  workday  daytofit  pred_orders\n",
      "0             101      1        0        1       102         74.0\n",
      "1             102      1        0        1       103         74.0\n",
      "2             103      1        0        1       104         75.0\n",
      "3             104      1        0        0       105         81.0\n",
      "4             105      1        0        0       106         81.0\n",
      "5             106      1        0        1       107         76.0\n",
      "6             107      1        0        1       108         76.0\n"
     ]
    }
   ],
   "source": [
    "## Wonder take home OA\n",
    "## Author: Huikun Zhang\n",
    "## Last modified on 1/15/2022\n",
    "\n",
    "\n",
    "## Question 1: Order Forecasting\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "#load data\n",
    "\n",
    "order = pd.read_csv(\"./a1/orders.csv\")\n",
    "# engineer date to week day and week\n",
    "order['Date'] = pd.to_datetime(order['Date'])\n",
    "order['DoW'] = order['Date'].dt.day_name()\n",
    "order['Week'] = order['Date'].dt.week\n",
    "\n",
    "# Feature engineering based on Exploratory Data Analysis -- see below\n",
    "order['outlier'] = np.where(order['Days Of launch'].isin([20,90]), 1, 0)\n",
    "order['workday'] = np.where(order['DoW'].isin(['Saturday', 'Sunday']), 0, 1)\n",
    "order['stage'] = np.where(order['Days Of launch'] > 70, 1, 0)\n",
    "order['daytofit'] = order['Days Of launch'] + 1\n",
    "\n",
    "X = order[['daytofit', 'stage', 'outlier', 'workday']]\n",
    "y = order['Number of Orders']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=2022, stratify=order['stage'])\n",
    "# fit model\n",
    "reg = LinearRegression().fit(X_train, y_train )\n",
    "print(reg.score(X_train, y_train))\n",
    "print(reg.coef_)\n",
    "\n",
    "# model evaluation\n",
    "\n",
    "print(\"train set\")\n",
    "y_pred = reg.predict(X_train)\n",
    "print(metrics.mean_absolute_error(y_pred, y_train))\n",
    "print(metrics.mean_squared_error(y_pred, y_train))\n",
    "print(np.sqrt(metrics.mean_squared_error(y_pred, y_train)))\n",
    "\n",
    "print(\"test set\")\n",
    "y_pred2 = reg.predict(X_test)\n",
    "print(metrics.mean_absolute_error(y_pred2, y_test))\n",
    "print(metrics.mean_squared_error(y_pred2, y_test))\n",
    "print(np.sqrt(metrics.mean_squared_error(y_pred2, y_test)))\n",
    "\n",
    "\"\"\"\n",
    "Performance on train and test set are close in terms of MAE and RMSE\n",
    "Actually 100 observations and 4 variables, chance of overfitting is small\n",
    "\n",
    "Above is suggested for problems that may have overfitting concerns while regularization methods are applied\n",
    "\n",
    "Next, I'll fit model using all 100 observations and make the prediction for the next week starting from 2021-08-18\n",
    "\"\"\"\n",
    "\n",
    "print(\"all data model\")\n",
    "reg_all = LinearRegression().fit(X, y)\n",
    "print(reg_all.score(X, y))\n",
    "print(reg_all.coef_)\n",
    "\n",
    "nextweek = pd.DataFrame({'Days Of launch': [101, 102, 103, 104, 105, 106, 107],\n",
    "                         'stage': [1, 1, 1, 1, 1, 1, 1],\n",
    "                        'outlier': [0, 0, 0, 0, 0, 0, 0],\n",
    "                        'workday': [1, 1, 1, 0, 0, 1, 1]})\n",
    "nextweek['daytofit'] = nextweek['Days Of launch'] + 1\n",
    "\n",
    "nextweek['pred_orders'] = np.round(reg_all.predict(nextweek[['daytofit', 'stage', 'outlier', 'workday']]))\n",
    "print(nextweek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nTopic #2 - outliers\\n\\n2 obvious outliers observed -- outlier affect the performance of regression model\\nday 90 and day 20 order.iloc[[20,90]] -- both at Sunday\\n\\nsee below - more outliers might be detected based on difference between two consecutive observation, suspitious observations might be worthy of discussion\\nDay 78, 82, 76, 86\\n\\nDepending on assumptions, different approaches might be taken\\n\\nOption 1 (chosen): Given we might want to keep the continuity of observations (days of launch)\\nOption 2: might be data defects during data construction/collection -- remove or use average of that week\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets take a look at the scatter plot\n",
    "\"\"\"\n",
    "plt.scatter(order['Days Of launch'], order['Number of Orders'], c=\"g\", alpha=0.5)\n",
    "plt.xlabel(\"Days of launch\")\n",
    "plt.ylabel(\"Number of Orders\")\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "sns.scatterplot(data=order, x=\"Days Of launch\", y=\"Number of Orders\", hue=\"outlier\")\n",
    "\n",
    "\"\"\"\n",
    "Topic #1 - systematic change\n",
    "\n",
    "observe linearly increasing pattern (especially from day 0 to day 70ish) of #orders as days since launch increase\n",
    "order.iloc[65:75]\n",
    "at day 71 - 2021-07-20, observe a jump, day difference is 64-38 = 26 --- this indicate a systemmatic change\n",
    "\n",
    "Action - create an indicator for this in regression model\n",
    "\n",
    "\"\"\" \n",
    "\n",
    "\"\"\"\n",
    "Topic #2 - outliers\n",
    "\n",
    "2 obvious outliers observed -- outlier affect the performance of regression model\n",
    "day 90 and day 20 order.iloc[[20,90]] -- both at Sunday\n",
    "\n",
    "see below - more outliers might be detected based on difference between two consecutive observation, suspitious observations might be worthy of discussion\n",
    "Day 78, 82, 76, 86\n",
    "\n",
    "Depending on assumptions, different approaches might be taken\n",
    "\n",
    "Option 1 (chosen): Given we might want to keep the continuity of observations (days of launch)\n",
    "Option 2: might be data defects during data construction/collection -- remove or use average of that week\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets deal with outliers first\n",
    "#difference between two consecutive observation could serve as the metric to detect outlier\n",
    "\n",
    "day_dis = order['Number of Orders'].diff()\n",
    "plt.hist(day_dis[1:])\n",
    "plt.show() \n",
    "\n",
    "#day_dis.sort_values(ascending=False).head(10)\n",
    "#Given we only observe abnormal increase in orders, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nFinding is different to the statement on hint\\n\\nData tells Saturday and Sunday often have more demands\\n\\n\\n'"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets take a look at boxplot by days of week\n",
    "\n",
    "sns.boxplot(y='Number of Orders', x='DoW', \n",
    "                 data=order)\n",
    "\n",
    "\"\"\"\n",
    "Finding is different to the statement on hint\n",
    "\n",
    "Data tells Saturday and Sunday often have more demands\n",
    "\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nafter removing 2 outliers\\nwe still observe Saturday has more demands, Sunday is slightly above workdays\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Number of Orders', x='DoW', \n",
    "                 data=order.drop([20,90]))\n",
    "\n",
    "\"\"\"\n",
    "after removing 2 outliers\n",
    "we still observe Saturday has more demands, Sunday is slightly above workdays\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fde3375ddd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Number of Orders', x='workday', \n",
    "                 data=order.drop([20,90]))\n",
    "\n",
    "# as expected"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}