AvocadoPriceRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avocado Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Understanding\n",
    "\n",
    "The aim of this project is to answer the following four questions:\n",
    "    1. Which region are the lowest and highest prices of Avocado?\n",
    "    2. What is the highest region of avocado production?\n",
    "    3. What is the average avocado prices in each year?\n",
    "    4. What is the average avocado volume in each year?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding\n",
    "\n",
    "The [Avocado dataset](https://www.kaggle.com/neuromusic/avocado-prices) was been used in this project.\n",
    "\n",
    "This dataset contains 13 columns:\n",
    "    1. Date - The date of the observation\n",
    "    2. AveragePrice: the average price of a single avocado\n",
    "    3. Total Volume: Total number of avocados sold\n",
    "    4. Total Bags: Total number  o bags\n",
    "    5. Small Bags: Total number of Small bags\n",
    "    6. Large Bags: Total number of Large bags\n",
    "    7. XLarge Bags: Total number of XLarge bags\n",
    "    8. type: conventional or organic\n",
    "    9. year: the year\n",
    "    10. region: the city or region of the observation\n",
    "    11. 4046: Total number of avocados with PLU 4046 sold\n",
    "    12. 4225: Total number of avocados with PLU 4225 sold\n",
    "    13. 4770: Total number of avocados with PLU 4770 sold\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('avocado.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18249 entries, 0 to 18248\n",
      "Data columns (total 14 columns):\n",
      "Unnamed: 0      18249 non-null int64\n",
      "Date            18249 non-null object\n",
      "AveragePrice    18249 non-null float64\n",
      "Total Volume    18249 non-null float64\n",
      "4046            18249 non-null float64\n",
      "4225            18249 non-null float64\n",
      "4770            18249 non-null float64\n",
      "Total Bags      18249 non-null float64\n",
      "Small Bags      18249 non-null float64\n",
      "Large Bags      18249 non-null float64\n",
      "XLarge Bags     18249 non-null float64\n",
      "type            18249 non-null object\n",
      "year            18249 non-null int64\n",
      "region          18249 non-null object\n",
      "dtypes: float64(9), int64(2), object(3)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0           0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1           1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2           2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "3           3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
       "4           4  2015-11-29          1.28      51039.60   941.48   43838.39   \n",
       "\n",
       "     4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0   48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1   58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2  130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "3   72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
       "4   75.78     6183.95     5986.26      197.69          0.0  conventional   \n",
       "\n",
       "   year  region  \n",
       "0  2015  Albany  \n",
       "1  2015  Albany  \n",
       "2  2015  Albany  \n",
       "3  2015  Albany  \n",
       "4  2015  Albany  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing value checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      0\n",
       "Date            0\n",
       "AveragePrice    0\n",
       "Total Volume    0\n",
       "4046            0\n",
       "4225            0\n",
       "4770            0\n",
       "Total Bags      0\n",
       "Small Bags      0\n",
       "Large Bags      0\n",
       "XLarge Bags     0\n",
       "type            0\n",
       "year            0\n",
       "region          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Unnamed: 0','4046','4225','4770','Date'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AveragePrice  Total Volume  Total Bags  Small Bags  Large Bags  \\\n",
       "0          1.33      64236.62     8696.87     8603.62       93.25   \n",
       "1          1.35      54876.98     9505.56     9408.07       97.49   \n",
       "2          0.93     118220.22     8145.35     8042.21      103.14   \n",
       "3          1.08      78992.15     5811.16     5677.40      133.76   \n",
       "4          1.28      51039.60     6183.95     5986.26      197.69   \n",
       "\n",
       "   XLarge Bags          type  year  region  \n",
       "0          0.0  conventional  2015  Albany  \n",
       "1          0.0  conventional  2015  Albany  \n",
       "2          0.0  conventional  2015  Albany  \n",
       "3          0.0  conventional  2015  Albany  \n",
       "4          0.0  conventional  2015  Albany  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answering questions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
"source": [
    "def get_avarage(df,column):\n",
    "    \"\"\"\n",
    "    Description: This function to return the average value of the column \n",
    "\n",
    "    Arguments:\n",
    "        df: the DataFrame. \n",
    "        column: the selected column. \n",
    "    Returns:\n",
    "        column's average \n",
    "    \"\"\"\n",
    "    return sum(df[column])/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
   "def get_avarge_between_two_columns(df,column1,column2):\n",
    "    \"\"\"\n",
    "    Description: This function calculate the average between two columns in the dataset\n",
    "\n",
    "    Arguments:\n",
    "        df: the DataFrame. \n",
    "        column1:the first column. \n",
    "        column2:the scond column.\n",
    "    Returns:\n",
    "        Sorted data for relation between column1 and column2\n",
    "    \"\"\"\n",
    "    \n",
    "    List=list(df[column1].unique())\n",
    "    average=[]\n",
    "\n",
    "    for i in List:\n",
    "        x=df[df[column1]==i]\n",
    "        column1_average= get_avarage(x,column2)\n",
    "        average.append(column1_average)\n",
    "\n",
    "    df_column1_column2=pd.DataFrame({'column1':List,'column2':average})\n",
    "    column1_column2_sorted_index=df_column1_column2.column2.sort_values(ascending=False).index.values\n",
    "    column1_column2_sorted_data=df_column1_column2.reindex(column1_column2_sorted_index)\n",
    "    \n",
    "    return column1_column2_sorted_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(data,xlabel,ylabel):\n",
    "    \"\"\"\n",
    "    Description: This function to draw a barplot\n",
    "\n",
    "    Arguments:\n",
    "        data: the DataFrame. \n",
    "        xlabel: the label of the first column. \n",
    "        ylabel: the label of the second column.\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "        \n",
    "    plt.figure(figsize=(15,5))\n",
    "    ax=sns.barplot(x=data.column1,y=data.column2,palette='rocket')\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.title(('Avarage '+ylabel+' of Avocado According to '+xlabel));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which region are the lowest and highest prices of Avocado?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = get_avarge_between_two_columns(df,'region','AveragePrice')\n",
    "plot(data1,'Region','Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Houston  is the region producing avocado with the lowest price.\n"
     ]
    }
   ],
   "source": [
    "print(data1['column1'].iloc[-1], \" is the region producing avocado with the lowest price.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the highest region of avocado production?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking if there are outlier values or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Figure: Boxplot repersenting outlier columns.')"
      ]
     },
     "execution_count": 13,
     "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": [
    "data2 = get_avarge_between_two_columns(df,'region','Total Volume')\n",
    "sns.boxplot(x=data2.column2).set_title(\"Figure: Boxplot repersenting outlier columns.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TotalUS is outlier value\n"
     ]
    }
   ],
   "source": [
    "outlier_region = data2[data2.column2>10000000]\n",
    "print(outlier_region['column1'].iloc[-1], \"is outlier value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove the outlier values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_region.index\n",
    "data2 = data2.drop(outlier_region.index,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data2,'Region','Volume')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the average avocado prices in each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data3 = get_avarge_between_two_columns(df,'year','AveragePrice')\n",
    "plot(data3,'year','Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the average avocado volume in each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data4 = get_avarge_between_two_columns(df,'year','Total Volume')\n",
    "plot(data4,'year','Volume')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We bulit the regrestion model by used [Linear regresion from sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) to predict the avocado price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing some column types to categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['region'] = df['region'].astype('category')\n",
    "df['region'] = df['region'].cat.codes\n",
    "\n",
    "df['type'] = df['type'].astype('category')\n",
    "df['type'] = df['type'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18249 entries, 0 to 18248\n",
      "Data columns (total 9 columns):\n",
      "AveragePrice    18249 non-null float64\n",
      "Total Volume    18249 non-null float64\n",
      "Total Bags      18249 non-null float64\n",
      "Small Bags      18249 non-null float64\n",
      "Large Bags      18249 non-null float64\n",
      "XLarge Bags     18249 non-null float64\n",
      "type            18249 non-null int8\n",
      "year            18249 non-null int64\n",
      "region          18249 non-null int8\n",
      "dtypes: float64(6), int64(1), int8(2)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AveragePrice  Total Volume  Total Bags  Small Bags  Large Bags  \\\n",
       "0          1.33      64236.62     8696.87     8603.62       93.25   \n",
       "1          1.35      54876.98     9505.56     9408.07       97.49   \n",
       "2          0.93     118220.22     8145.35     8042.21      103.14   \n",
       "3          1.08      78992.15     5811.16     5677.40      133.76   \n",
       "4          1.28      51039.60     6183.95     5986.26      197.69   \n",
       "\n",
       "   XLarge Bags  type  year  region  \n",
       "0          0.0     0  2015       0  \n",
       "1          0.0     0  2015       0  \n",
       "2          0.0     0  2015       0  \n",
       "3          0.0     0  2015       0  \n",
       "4          0.0     0  2015       0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into X and y\n",
    "X = df.drop(['AveragePrice'],axis=1)\n",
    "y = df['AveragePrice']\n",
    "\n",
    "# split data into traing and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set: (12774, 8)  -  12774  samples\n",
      "testing set: (5475, 8)  -  5475  samples\n"
     ]
    }
   ],
   "source": [
    "print(\"training set:\",X_train.shape,' - ',y_train.shape[0],' samples')\n",
    "print(\"testing set:\",X_test.shape,' - ',y_test.shape[0],' samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bulid and fit the model\n",
    "model = LinearRegression(normalize=True)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of testing dataset  38.58074176429158\n"
     ]
    }
   ],
   "source": [
    "# prediction and calculate the accuracy for the testing dataset\n",
    "test_pre = model.predict(X_test)\n",
    "test_score = r2_score(y_test,test_pre)\n",
    "print(\"The accuracy of testing dataset \",test_score*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of training dataset  39.70686042411577\n"
     ]
    }
   ],
   "source": [
    "# prediction and calculate the accuracy for the testing dataset\n",
    "train_pre = model.predict(X_train)\n",
    "train_score = r2_score(y_train,train_pre)\n",
    "print(\"The accuracy of training dataset \",train_score*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model doesn't work well with this dataset, In order to the avocado prices were near together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}