Practice Project 15 (Wholesale customers Data Set).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wholesale customers Data Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1) FRESH: annual spending (m.u.) on fresh products (Continuous);\n",
    "2) MILK: annual spending (m.u.) on milk products (Continuous);\n",
    "3) GROCERY: annual spending (m.u.)on grocery products (Continuous);\n",
    "4) FROZEN: annual spending (m.u.)on frozen products (Continuous)\n",
    "5) DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous)\n",
    "6) DELICATESSEN: annual spending (m.u.)on and delicatessen products (Continuous);\n",
    "7) CHANNEL: customersâ€™ Channel - Horeca (Hotel/Restaurant/CafÃ©) or Retail channel (Nominal)\n",
    "8) REGION: customersâ€™ Region â€“ Lisnon, Oporto or Other (Nominal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Wholesale customers data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['Channel','Region'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'Delicassen':'Total_amount_spend'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Total_amount_spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Total_amount_spend\n",
       "0  12669  9656     7561     214              2674                1338\n",
       "1   7057  9810     9568    1762              3293                1776\n",
       "2   6353  8808     7684    2405              3516                7844\n",
       "3  13265  1196     4221    6404               507                1788\n",
       "4  22615  5410     7198    3915              1777                5185"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,df.columns!='Total_amount_spend']\n",
    "y=df.iloc[:,df.columns=='Total_amount_spend'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaling=MinMaxScaler()\n",
    "x_scaled=scaling.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x_scaled,y,test_size=.20,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model1=LinearRegression()\n",
    "model1.fit(x_train,y_train)\n",
    "y_pred=model1.predict(x_test)\n",
    "df1=pd.DataFrame({'ACTUAL VALUES':y_test.flatten(),'PREDICTED VALUES':y_pred.flatten()})\n",
    "df2=df1.head(30)\n",
    "df2.plot(kind='bar',figsize=(16,14))\n",
    "plt.grid(color='green',linewidth='0.5',linestyle='-',which='major')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}