Target Store Sales Prediction.ipynb

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  {
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   "source": [
    "# Target Store Sales Prediction\n",
    "#### Content:\n",
    "Here provided with historical sales data for 45 stores located in different region search store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.\n",
    "\n",
    "#### Problem description: \n",
    "One challenge of modeling retail data is the need to make decisions based on limited history. Holidays and select major events come once a year, and so does the chance to see how strategic decisions impacted the bottom line. In addition, markdowns are known to affect sales the challenge is to predict which departments will be affected and to what extent.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "214e067b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraires\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4018fe3",
   "metadata": {},
   "source": [
    "### Importing all the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c9f7df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1= pd.read_csv('Business_Data.csv') #Business_Data\n",
    "df2= pd.read_csv('Sales_History.csv') #Sales_History\n",
    "df3= pd.read_csv('Store_Details.csv') #Store_Details"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b4cbbc",
   "metadata": {},
   "source": [
    "### Approach:\n",
    "- **Understand the data variables properly**\n",
    "- **Cleaning the data:** Cleaning the data, that is, fill the missing values (if any), treat the outliers (or odd values), etc. Ensureing each variable’s data is as per the nature of the variable (e.g. Date field should contain only date values – can extract year, month and day of the week, and numeric column should be formatted as numeric, etc.)\n",
    "- **Conducting EDA (Exploratory Data Analysis) on the cleaned Data**\n",
    "- **Uni-variate and Bi-variate Analysis:** Checking the distribution of independent variables and also compare them with the dependent variable.\n",
    "- **Feature Engineering**\n",
    "- **Hypothesis Testing**\n",
    "- **Identify the most important variables (or data parameters) that affect the final decision:** Identify the impact of each variable on the final result graphically (correlation / scatter plots, regression plots, etc.). Keeping those variables that affect the final outcome\n",
    "- **Develop and Validate Samples**\n",
    "- **Model Building**\n",
    "- **Improving model accuracy:** We know that machine learning algorithms are driven by parameters. These parameters majorly influence the outcome of learning process. So, find the optimum value for each parameter to improve the accuracy of the model and repeat this process with a number of well performing models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b58075",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5289c171",
   "metadata": {},
   "source": [
    "1. Sales history - Historical sales data of each store from 30th April 2017 to 25th October 2019 having 421570 data points and 5 columns.\n",
    "2. Store details - Anonymized information about 45 stores indicating store type, Address, Location and\n",
    "size of store.\n",
    "3. Business data - Contains additional data related to the store, department, and regional activity for the\n",
    "given dates.\n",
    "\n",
    "Lets look at the head of all three dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8441f239",
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   "outputs": [
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       "   Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
       "0      1   4/30/2017        42.31       2.572        NaN        NaN   \n",
       "1      1  11/30/2017        38.51       2.548        NaN        NaN   \n",
       "2      1   2/17/2017        39.93       2.514        NaN        NaN   \n",
       "3      1   2/24/2017        46.63       2.561        NaN        NaN   \n",
       "4      1    5/1/2017        46.50       2.625        NaN        NaN   \n",
       "\n",
       "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment_Rate  Holiday  \n",
       "0        NaN        NaN        NaN  211.096358              8.106    False  \n",
       "1        NaN        NaN        NaN  211.242170              8.106     True  \n",
       "2        NaN        NaN        NaN  211.289143              8.106    False  \n",
       "3        NaN        NaN        NaN  211.319643              8.106    False  \n",
       "4        NaN        NaN        NaN  211.350143              8.106    False  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df1.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dc7f1ee",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Department</th>\n",
       "      <th>Date</th>\n",
       "      <th>Total_Sales</th>\n",
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       "   Store  Department        Date  Total_Sales  Holiday\n",
       "0      1           1   4/30/2017     24924.50    False\n",
       "1      1           1  11/30/2017     46039.49     True\n",
       "2      1           1   2/17/2017     41595.55    False\n",
       "3      1           1   2/24/2017     19403.54    False\n",
       "4      1           1  05-01-2017     21827.90    False"
      ]
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "df2.head()"
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  {
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   "execution_count": 5,
   "id": "682eeb2c",
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       "      <th>Store</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1893 W Malvern Ave, Fullerton, California</td>\n",
       "      <td>92835</td>\n",
       "      <td>Applegate Ranch Shopping Cente</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1000 Commerce Ave, Atwater, California</td>\n",
       "      <td>95301</td>\n",
       "      <td>Bayfair Cente</td>\n",
       "      <td>202307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Food</td>\n",
       "      <td>15555 East 14th Street, San Leandro, California</td>\n",
       "      <td>94578</td>\n",
       "      <td>Capitola Mal</td>\n",
       "      <td>37392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1855 41st Avenue, Capitola, California</td>\n",
       "      <td>95010</td>\n",
       "      <td>Chino Spectrum Marketplace�&amp; Towne Cente</td>\n",
       "      <td>205863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Food</td>\n",
       "      <td>3800-4046 Grand Ave.&amp; 3801-4097 Grand Ave., Ch...</td>\n",
       "      <td>91710</td>\n",
       "      <td>Eagle Rock Plaz</td>\n",
       "      <td>34875</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Store                    Type  \\\n",
       "0      1  E-Commerce Fulfillment   \n",
       "1      2  E-Commerce Fulfillment   \n",
       "2      3                    Food   \n",
       "3      4  E-Commerce Fulfillment   \n",
       "4      5                    Food   \n",
       "\n",
       "                                             Address  Area_Code  \\\n",
       "0          1893 W Malvern Ave, Fullerton, California      92835   \n",
       "1             1000 Commerce Ave, Atwater, California      95301   \n",
       "2    15555 East 14th Street, San Leandro, California      94578   \n",
       "3             1855 41st Avenue, Capitola, California      95010   \n",
       "4  3800-4046 Grand Ave.& 3801-4097 Grand Ave., Ch...      91710   \n",
       "\n",
       "                                   Location    Size  \n",
       "0            Applegate Ranch Shopping Cente  151315  \n",
       "1                             Bayfair Cente  202307  \n",
       "2                              Capitola Mal   37392  \n",
       "3  Chino Spectrum Marketplace�& Towne Cente  205863  \n",
       "4                           Eagle Rock Plaz   34875  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af861118",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8190, 12)\n",
      "(421570, 5)\n",
      "(45, 6)\n"
     ]
    }
   ],
   "source": [
    "# Lets look at the shape of all three dataset\n",
    "print(df1.shape) #business data\n",
    "print(df2.shape) #sales history\n",
    "print(df3.shape) #Store details"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6200be18",
   "metadata": {},
   "source": [
    "It is very important to merges these data on same column ['Store', 'Date']. We are going to mergred first df1 which is business data and df2 which is sales history data.\n",
    "\n",
    "Before going to merged these data, first we will group all the sales hstory data on date, because there are lots of transaction happend on same date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b047b79",
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "<p>2955 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "        Store  Department       Date  Total_Sales  Holiday\n",
       "0           1           1  4/30/2017     24924.50    False\n",
       "143         1           2  4/30/2017     50605.27    False\n",
       "286         1           3  4/30/2017     13740.12    False\n",
       "429         1           4  4/30/2017     39954.04    False\n",
       "572         1           5  4/30/2017     32229.38    False\n",
       "...       ...         ...        ...          ...      ...\n",
       "420727     45          92  4/30/2017     68203.08    False\n",
       "420870     45          93  4/30/2017      4002.34    False\n",
       "421147     45          95  4/30/2017     63833.63    False\n",
       "421292     45          97  4/30/2017      8393.22    False\n",
       "421435     45          98  4/30/2017       347.23    False\n",
       "\n",
       "[2955 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking how many transaction happend on same date 4/30/2017\n",
    "df2[df2['Date']=='4/30/2017']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c603b246",
   "metadata": {},
   "source": [
    "Before going to grouping as we seen in Sales History (df2) has Two garbage columns 'Holiday' and 'Department', in business data (df1) Holiday column is already there so, we will remove from df2. There is no use of 'Department' column in model building, so we will also remove it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2df914eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the garbage columns from df2\n",
    "df2.drop(['Holiday','Department'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f40c0824",
   "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>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Total_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01-02-2018</td>\n",
       "      <td>1495064.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>01-04-2019</td>\n",
       "      <td>1624477.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>01-05-2018</td>\n",
       "      <td>1488538.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>01-08-2017</td>\n",
       "      <td>1453329.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>01-11-2019</td>\n",
       "      <td>1459601.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Total_Sales\n",
       "0      1  01-02-2018   1495064.75\n",
       "1      1  01-04-2019   1624477.58\n",
       "2      1  01-05-2018   1488538.09\n",
       "3      1  01-08-2017   1453329.50\n",
       "4      1  01-11-2019   1459601.17"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we will group the df2 on date columns\n",
    "df2= df2.groupby(['Store','Date']).agg({'Total_Sales': 'sum'}).reset_index()\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "195651a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6435, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets see the shape of df2\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69d224fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Type</th>\n",
       "      <th>Address</th>\n",
       "      <th>Area_Code</th>\n",
       "      <th>Location</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4/30/2017</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1893 W Malvern Ave, Fullerton, California</td>\n",
       "      <td>92835</td>\n",
       "      <td>Applegate Ranch Shopping Cente</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11/30/2017</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1893 W Malvern Ave, Fullerton, California</td>\n",
       "      <td>92835</td>\n",
       "      <td>Applegate Ranch Shopping Cente</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2/17/2017</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1893 W Malvern Ave, Fullerton, California</td>\n",
       "      <td>92835</td>\n",
       "      <td>Applegate Ranch Shopping Cente</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2/24/2017</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1893 W Malvern Ave, Fullerton, California</td>\n",
       "      <td>92835</td>\n",
       "      <td>Applegate Ranch Shopping Cente</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5/1/2017</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>1893 W Malvern Ave, Fullerton, California</td>\n",
       "      <td>92835</td>\n",
       "      <td>Applegate Ranch Shopping Cente</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
       "0      1   4/30/2017        42.31       2.572        NaN        NaN   \n",
       "1      1  11/30/2017        38.51       2.548        NaN        NaN   \n",
       "2      1   2/17/2017        39.93       2.514        NaN        NaN   \n",
       "3      1   2/24/2017        46.63       2.561        NaN        NaN   \n",
       "4      1    5/1/2017        46.50       2.625        NaN        NaN   \n",
       "\n",
       "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment_Rate  Holiday  \\\n",
       "0        NaN        NaN        NaN  211.096358              8.106    False   \n",
       "1        NaN        NaN        NaN  211.242170              8.106     True   \n",
       "2        NaN        NaN        NaN  211.289143              8.106    False   \n",
       "3        NaN        NaN        NaN  211.319643              8.106    False   \n",
       "4        NaN        NaN        NaN  211.350143              8.106    False   \n",
       "\n",
       "   Total_Sales                    Type  \\\n",
       "0   1643690.90  E-Commerce Fulfillment   \n",
       "1   1641957.44  E-Commerce Fulfillment   \n",
       "2   1611968.17  E-Commerce Fulfillment   \n",
       "3   1409727.59  E-Commerce Fulfillment   \n",
       "4          NaN  E-Commerce Fulfillment   \n",
       "\n",
       "                                     Address  Area_Code  \\\n",
       "0  1893 W Malvern Ave, Fullerton, California      92835   \n",
       "1  1893 W Malvern Ave, Fullerton, California      92835   \n",
       "2  1893 W Malvern Ave, Fullerton, California      92835   \n",
       "3  1893 W Malvern Ave, Fullerton, California      92835   \n",
       "4  1893 W Malvern Ave, Fullerton, California      92835   \n",
       "\n",
       "                         Location    Size  \n",
       "0  Applegate Ranch Shopping Cente  151315  \n",
       "1  Applegate Ranch Shopping Cente  151315  \n",
       "2  Applegate Ranch Shopping Cente  151315  \n",
       "3  Applegate Ranch Shopping Cente  151315  \n",
       "4  Applegate Ranch Shopping Cente  151315  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging all 3 datasets based on common columns\n",
    "df1_df2 = pd.merge(df1, df2, how = 'left', left_on=['Store','Date'], right_on = ['Store','Date'])\n",
    "df = pd.merge(df1_df2, df3, on = 'Store', how = 'left')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b1d374e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8190, 18)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets look at the shape of final data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7f9fe89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# After replacing all NA values with zero in each \"MarkDown\" column (NA stands for \"no discount\"), add all markdowns.\n",
    "for i in ['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5']:\n",
    "  df[i] = df[i].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f758c1c0",
   "metadata": {},
   "source": [
    "The garbage columns \"Store,\" \"Address,\" \"Area Code,\" and \"Location\" do not contribute anything to the model columns, which are unique information for each store (stores 1 through 45) in the form of huge texts and numbers. Because we already take shop size into account, there is no need to include these columns, hence they will be removed. \n",
    "\n",
    "Also, we are going to add all the MarksDowns which is discount given on particular dates and names a new column which is Totaol_markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e929d30c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-c198bd29df19>:5: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  df['weeknumber']= pd.to_datetime(df['Date'],errors ='coerce').dt.week\n"
     ]
    }
   ],
   "source": [
    "# Replacing the Column Holidays , False =NO, and True=YES\n",
    "df['Holiday']=np.where(df['Holiday']==False,\"No\",\"Yes\")\n",
    "\n",
    "# converting the column of Date into Datetime datatype and extracting week number from date columns\n",
    "df['weeknumber']= pd.to_datetime(df['Date'],errors ='coerce').dt.week\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7812087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>weeknumber</th>\n",
       "      <th>Total_MarkDown</th>\n",
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       "      <th>0</th>\n",
       "      <td>42.31</td>\n",
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       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>48</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  Fuel_Price         CPI  Unemployment_Rate Holiday  \\\n",
       "0        42.31       2.572  211.096358              8.106      No   \n",
       "1        38.51       2.548  211.242170              8.106     Yes   \n",
       "2        39.93       2.514  211.289143              8.106      No   \n",
       "3        46.63       2.561  211.319643              8.106      No   \n",
       "4        46.50       2.625  211.350143              8.106      No   \n",
       "\n",
       "   Total_Sales                    Type    Size  weeknumber  Total_MarkDown  \n",
       "0   1643690.90  E-Commerce Fulfillment  151315          17             0.0  \n",
       "1   1641957.44  E-Commerce Fulfillment  151315          48             0.0  \n",
       "2   1611968.17  E-Commerce Fulfillment  151315           7             0.0  \n",
       "3   1409727.59  E-Commerce Fulfillment  151315           8             0.0  \n",
       "4          NaN  E-Commerce Fulfillment  151315          18             0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding all the markdown 1 to 5:\n",
    "df['Total_MarkDown']= df['MarkDown1']+df['MarkDown2']+df['MarkDown3']+df['MarkDown4']+df['MarkDown5']\n",
    "\n",
    "# droping the useless columns \n",
    "df.drop(['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5','Date','Store','Address', 'Area_Code', 'Location'], inplace = True,axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e11e598b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Size</th>\n",
       "      <th>weeknumber</th>\n",
       "      <th>Total_MarkDown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8190.000000</td>\n",
       "      <td>8190.000000</td>\n",
       "      <td>7605.000000</td>\n",
       "      <td>7605.000000</td>\n",
       "      <td>3.780000e+03</td>\n",
       "      <td>8190.000000</td>\n",
       "      <td>8190.000000</td>\n",
       "      <td>8190.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.356198</td>\n",
       "      <td>3.405992</td>\n",
       "      <td>172.460809</td>\n",
       "      <td>7.826821</td>\n",
       "      <td>1.042570e+06</td>\n",
       "      <td>130287.600000</td>\n",
       "      <td>25.851648</td>\n",
       "      <td>8881.703448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.678607</td>\n",
       "      <td>0.431337</td>\n",
       "      <td>39.738346</td>\n",
       "      <td>1.877259</td>\n",
       "      <td>5.721346e+05</td>\n",
       "      <td>63115.971429</td>\n",
       "      <td>14.788965</td>\n",
       "      <td>19084.538888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.290000</td>\n",
       "      <td>2.472000</td>\n",
       "      <td>126.064000</td>\n",
       "      <td>3.684000</td>\n",
       "      <td>2.135383e+05</td>\n",
       "      <td>34875.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1692.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45.902500</td>\n",
       "      <td>3.041000</td>\n",
       "      <td>132.364839</td>\n",
       "      <td>6.634000</td>\n",
       "      <td>5.463774e+05</td>\n",
       "      <td>70713.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.710000</td>\n",
       "      <td>3.513000</td>\n",
       "      <td>182.764003</td>\n",
       "      <td>7.806000</td>\n",
       "      <td>9.537690e+05</td>\n",
       "      <td>126512.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73.880000</td>\n",
       "      <td>3.743000</td>\n",
       "      <td>213.932412</td>\n",
       "      <td>8.567000</td>\n",
       "      <td>1.401411e+06</td>\n",
       "      <td>202307.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>11819.642500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.950000</td>\n",
       "      <td>4.468000</td>\n",
       "      <td>228.976456</td>\n",
       "      <td>14.313000</td>\n",
       "      <td>3.818686e+06</td>\n",
       "      <td>219622.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>783529.450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature   Fuel_Price          CPI  Unemployment_Rate   Total_Sales  \\\n",
       "count  8190.000000  8190.000000  7605.000000        7605.000000  3.780000e+03   \n",
       "mean     59.356198     3.405992   172.460809           7.826821  1.042570e+06   \n",
       "std      18.678607     0.431337    39.738346           1.877259  5.721346e+05   \n",
       "min      -7.290000     2.472000   126.064000           3.684000  2.135383e+05   \n",
       "25%      45.902500     3.041000   132.364839           6.634000  5.463774e+05   \n",
       "50%      60.710000     3.513000   182.764003           7.806000  9.537690e+05   \n",
       "75%      73.880000     3.743000   213.932412           8.567000  1.401411e+06   \n",
       "max     101.950000     4.468000   228.976456          14.313000  3.818686e+06   \n",
       "\n",
       "                Size   weeknumber  Total_MarkDown  \n",
       "count    8190.000000  8190.000000     8190.000000  \n",
       "mean   130287.600000    25.851648     8881.703448  \n",
       "std     63115.971429    14.788965    19084.538888  \n",
       "min     34875.000000     1.000000    -1692.480000  \n",
       "25%     70713.000000    13.000000        0.000000  \n",
       "50%    126512.000000    25.000000        0.000000  \n",
       "75%    202307.000000    38.000000    11819.642500  \n",
       "max    219622.000000    52.000000   783529.450000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304d2d10",
   "metadata": {},
   "source": [
    "From above table we conclude that:\n",
    "- The average Temeprature is around 59.35, min= 7.290000, max=  101.950000\n",
    "- The average CPI value is around 172\n",
    "- The average Unemployment_Rate is around 7.826821 which is not too bad\n",
    "- The Total_MarkDown which is discount given on that particular date, it should not be negative \n",
    "\n",
    "We will look at the neagative discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3d453ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>weeknumber</th>\n",
       "      <th>Total_MarkDown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5437</th>\n",
       "      <td>49.09</td>\n",
       "      <td>3.597</td>\n",
       "      <td>224.054703</td>\n",
       "      <td>6.237</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regional</td>\n",
       "      <td>42988</td>\n",
       "      <td>8</td>\n",
       "      <td>-1692.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6518</th>\n",
       "      <td>68.04</td>\n",
       "      <td>3.204</td>\n",
       "      <td>222.325549</td>\n",
       "      <td>6.228</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>39910</td>\n",
       "      <td>28</td>\n",
       "      <td>-80.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Temperature  Fuel_Price         CPI  Unemployment_Rate Holiday  \\\n",
       "5437        49.09       3.597  224.054703              6.237      No   \n",
       "6518        68.04       3.204  222.325549              6.228      No   \n",
       "\n",
       "      Total_Sales                    Type   Size  weeknumber  Total_MarkDown  \n",
       "5437          NaN                Regional  42988           8        -1692.48  \n",
       "6518          NaN  E-Commerce Fulfillment  39910          28          -80.11  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Total_MarkDown']<0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a05e31e",
   "metadata": {},
   "source": [
    "As we seen from above data there are two rows where there is neagtive discount, we will remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c02aece8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Size</th>\n",
       "      <th>weeknumber</th>\n",
       "      <th>Total_MarkDown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8188.000000</td>\n",
       "      <td>8188.000000</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>3.780000e+03</td>\n",
       "      <td>8188.000000</td>\n",
       "      <td>8188.000000</td>\n",
       "      <td>8188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.356391</td>\n",
       "      <td>3.405993</td>\n",
       "      <td>172.447465</td>\n",
       "      <td>7.827240</td>\n",
       "      <td>1.042570e+06</td>\n",
       "      <td>130309.299707</td>\n",
       "      <td>25.853566</td>\n",
       "      <td>8884.089378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.680297</td>\n",
       "      <td>0.431378</td>\n",
       "      <td>39.735050</td>\n",
       "      <td>1.877327</td>\n",
       "      <td>5.721346e+05</td>\n",
       "      <td>63108.398424</td>\n",
       "      <td>14.789436</td>\n",
       "      <td>19086.254913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.290000</td>\n",
       "      <td>2.472000</td>\n",
       "      <td>126.064000</td>\n",
       "      <td>3.684000</td>\n",
       "      <td>2.135383e+05</td>\n",
       "      <td>34875.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45.900000</td>\n",
       "      <td>3.041000</td>\n",
       "      <td>132.364839</td>\n",
       "      <td>6.634000</td>\n",
       "      <td>5.463774e+05</td>\n",
       "      <td>70713.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.710000</td>\n",
       "      <td>3.513000</td>\n",
       "      <td>182.716838</td>\n",
       "      <td>7.806000</td>\n",
       "      <td>9.537690e+05</td>\n",
       "      <td>126512.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73.880000</td>\n",
       "      <td>3.743000</td>\n",
       "      <td>213.932412</td>\n",
       "      <td>8.567000</td>\n",
       "      <td>1.401411e+06</td>\n",
       "      <td>202307.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>11824.377500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.950000</td>\n",
       "      <td>4.468000</td>\n",
       "      <td>228.976456</td>\n",
       "      <td>14.313000</td>\n",
       "      <td>3.818686e+06</td>\n",
       "      <td>219622.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>783529.450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature   Fuel_Price          CPI  Unemployment_Rate   Total_Sales  \\\n",
       "count  8188.000000  8188.000000  7603.000000        7603.000000  3.780000e+03   \n",
       "mean     59.356391     3.405993   172.447465           7.827240  1.042570e+06   \n",
       "std      18.680297     0.431378    39.735050           1.877327  5.721346e+05   \n",
       "min      -7.290000     2.472000   126.064000           3.684000  2.135383e+05   \n",
       "25%      45.900000     3.041000   132.364839           6.634000  5.463774e+05   \n",
       "50%      60.710000     3.513000   182.716838           7.806000  9.537690e+05   \n",
       "75%      73.880000     3.743000   213.932412           8.567000  1.401411e+06   \n",
       "max     101.950000     4.468000   228.976456          14.313000  3.818686e+06   \n",
       "\n",
       "                Size   weeknumber  Total_MarkDown  \n",
       "count    8188.000000  8188.000000     8188.000000  \n",
       "mean   130309.299707    25.853566     8884.089378  \n",
       "std     63108.398424    14.789436    19086.254913  \n",
       "min     34875.000000     1.000000        0.000000  \n",
       "25%     70713.000000    13.000000        0.000000  \n",
       "50%    126512.000000    25.000000        0.000000  \n",
       "75%    202307.000000    38.000000    11824.377500  \n",
       "max    219622.000000    52.000000   783529.450000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Droping all the Total_MarkDown  values  less than 0 in columns 'Total_MarkDown'\n",
    "df.drop(df.index[df['Total_MarkDown'] < 0], inplace = True)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b748c80c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Temperature          0.000000\n",
       "Fuel_Price           0.000000\n",
       "CPI                  0.071446\n",
       "Unemployment_Rate    0.071446\n",
       "Holiday              0.000000\n",
       "Total_Sales          0.538349\n",
       "Type                 0.000000\n",
       "Size                 0.000000\n",
       "weeknumber           0.000000\n",
       "Total_MarkDown       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total missing values in the dataset\n",
    "df.isnull().sum()/len(df) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "531dfb81",
   "metadata": {},
   "source": [
    "- In Column 'CPI' total 7.14 % mising values\n",
    "- in column 'Unemployment_Rate' also have 7.14 % mising values \n",
    "- Column \"Total_Sales\" has 53.8 % missing values but these values are representing those rows where we need to predict \"Total_sales\" after model building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e195a3df",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Handling the missing values in column CPI \n",
    "df['CPI'].fillna(df1['CPI'].mean(),inplace=True)\n",
    "\n",
    "# Handling the missing values in column Unemployment_Rate \n",
    "df['Unemployment_Rate'].fillna(df1['Unemployment_Rate'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b88777",
   "metadata": {},
   "source": [
    "### Outliers Treatement \n",
    "- Before going to build the model outliers should be treated, otherwise it will impact the on final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b5c05cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continuous variables and categorical variables\n",
    "Cont_cols = df[[\"Temperature\", \"Fuel_Price\", \"CPI\", \"Unemployment_Rate\", \"Total_Sales\", \"Size\", \"Total_MarkDown\"]]\n",
    "cat_cols = df[[\"Holiday\", \"Type\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "001d8676",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to return outliers\n",
    "def detect_outlier(data):\n",
    "    outliers=[]\n",
    "    threshold = 3\n",
    "    mean = np.mean(data)\n",
    "    std =np.std(data)\n",
    "    \n",
    "    \n",
    "    for y in data:\n",
    "        z_score= (y - mean)/std \n",
    "        if np.abs(z_score) > threshold:\n",
    "            outliers.append(y)\n",
    "    return outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5dd9a162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot plot to check if there are any outliers in the 'Temperature' column \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.boxplot(df['Temperature'])\n",
    "plt.title(\"Boxplot for 'Temperature' column\",fontsize=15)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "17431615",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers for 'Temperature' column are :  [-2.06, 2.32, -6.08, -6.61, -7.29, 2.45, 0.25]\n"
     ]
    }
   ],
   "source": [
    "# Outliers in 'Temperature' column\n",
    "outlier_data = detect_outlier(df['Temperature'])\n",
    "print(\"Outliers for 'Temperature' column are : \", outlier_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc21878a",
   "metadata": {},
   "source": [
    "Temperature might goes below the zero, as we seen min temperature is around -7, which is not a outliers.  Because, a temperature value of  [-2.06, 2.32, -6.08, -6.61, -7.29, 2.45, 0.25] is valid Temperature so removing outliers here is not a good option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3a59165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot plot to check if there are any outliers in the 'Temperature' column \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.boxplot(df['Fuel_Price'])\n",
    "plt.title(\"Boxplot for 'Fuel_Price' column\",fontsize=15)\n",
    "plt.show()  # no utliers found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf81c944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot plot to check if there are any outliers in the 'Temperature' column \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.boxplot(df['CPI'])\n",
    "plt.title(\"Boxplot for 'CPI' column\",fontsize=15)\n",
    "plt.show()  # no utliers found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a986ceff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot plot to check if there are any outliers in the 'Temperature' column \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.boxplot(df['Unemployment_Rate'])\n",
    "plt.title(\"Boxplot for 'Unemployment_Rate' column\",fontsize=15)\n",
    "plt.show()  # no utliers found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "49a31fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers for 'Unemployment_Rate' column are :  [13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 13.975, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.099, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.18, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.313, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 14.021, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.736, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503, 13.503]\n"
     ]
    }
   ],
   "source": [
    "# Outliers in 'Unemployment_Rate' column\n",
    "outlier_data = detect_outlier(df['Unemployment_Rate'])\n",
    "print(\"Outliers for 'Unemployment_Rate' column are : \", outlier_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113884e0",
   "metadata": {},
   "source": [
    "We are not going to remove these outliers, beacause the Unemployment_Rate might seems to be okay and it might goes to 14."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e22b66ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to check if there are any outliers in the 'Size' column \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.boxplot(df['Size'])\n",
    "plt.title(\"Boxplot for 'Size' column\",fontsize=15)\n",
    "plt.show()  # no utliers found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84f1957e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to check if there are any outliers in the 'Total_markdown' column \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.boxplot(df['Total_MarkDown'])\n",
    "plt.title(\"Boxplot for 'Total_MarkDown' column\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d35ef67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers for 'Total_MarkDown' column are :  [76351.06999999999, 76311.31000000001, 98974.87, 80045.4, 105528.0, 134773.50999999998, 109611.15000000001, 73717.26, 117660.84, 93322.93, 82098.40000000001, 110681.95, 101118.65999999999, 110853.45, 73561.14000000001, 84794.95, 92849.95, 74133.28, 115281.12999999999, 67107.66, 66563.21, 106926.52000000002, 77418.75, 78684.32999999999, 143223.31, 117682.51999999999, 83583.57, 149976.71, 79820.62999999999, 119437.66, 73367.27, 96513.93000000001, 85532.16, 122659.41, 70708.06, 70669.22, 133315.06, 66720.95999999999, 101355.79, 115446.52, 75070.48000000001, 114565.27, 70523.39, 100916.17, 126999.28, 66448.95, 69704.73, 153504.44, 75974.07, 178693.13999999998, 91671.96, 83143.31, 67395.51, 143777.92, 66197.91, 108493.39, 116118.54, 67634.55, 78258.6, 67583.15000000001, 78646.05, 96601.45, 106255.08, 72758.08, 69881.05, 81094.48, 75210.07999999999, 101206.8, 93464.21999999999, 117920.3, 118242.04, 66982.93000000001, 69327.23, 151904.86000000002, 104110.89, 77843.59, 109430.24, 129826.79999999999, 67883.09, 142144.53, 76408.52000000002, 162215.21000000002, 129302.27, 77011.65, 68163.77, 160510.61000000002, 78509.26000000001, 117395.42000000001, 104793.96, 90430.76, 134270.43, 80046.29999999999, 132521.80000000002, 70826.98, 124063.01000000001, 85429.91, 134790.92, 90881.76999999999, 80531.81, 76205.91, 73920.90000000001, 74690.31999999999, 142563.99000000002, 71678.44, 68199.67, 105689.59000000001, 83854.23, 108645.33999999998, 66157.93, 104113.29000000001, 783529.45, 87218.07, 105268.55, 77651.58, 104368.52, 110646.09, 74378.05, 87176.4, 85667.88, 107592.2, 139280.71, 66304.31, 110659.29999999999, 89088.16, 90500.67000000001, 140569.99000000002, 82596.35, 68197.64, 102094.99, 91395.65999999999, 125536.02, 113752.52, 74552.97, 125001.4, 109556.21999999999, 91813.19, 75913.41, 115812.95, 82999.08, 70357.76000000001, 68273.42, 83589.44999999998, 99900.31999999999, 68113.75, 101378.85999999999, 138028.38, 83809.57, 74652.61999999998, 81978.85]\n"
     ]
    }
   ],
   "source": [
    "# Outliers in 'Total_markdown' column\n",
    "outlier_data = detect_outlier(df['Total_MarkDown'])\n",
    "print(\"Outliers for 'Total_MarkDown' column are : \", outlier_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9b251e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The Total_MarkDown distributions')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.distplot(df['Total_MarkDown'])\n",
    "plt.xlabel('The Total_MarkDown',fontsize=15)\n",
    "plt.ylabel('count',fontsize=15)\n",
    "plt.title('The Total_MarkDown distributions',fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6bd417e",
   "metadata": {},
   "source": [
    "There appear to be a lot of positive outliers here as well, however this is not the case. Because the frequency of 0 \"Total markdown\" is so high, all other values are treated as outliers, as can be seen in the graph below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "40d45155",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining continuous and categorical columns and converting \"Total markdown\" column as high and low\n",
    "median_value = df.median()['Total_MarkDown']\n",
    "df['Total_MarkDown'] = np.where(df['Total_MarkDown'] > median_value , \"High\", \"Low\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "22525bab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continuous variables and categorical variables\n",
    "Cont_cols = df[[\"Temperature\", \"Fuel_Price\", \"CPI\", \"Unemployment_Rate\", \"Total_Sales\", \"Size\"]]\n",
    "cat_cols = df[[\"Holiday\", \"Type\",\"Total_MarkDown\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68519497",
   "metadata": {},
   "source": [
    "## EDA (Exploratory Data Analysis) on the cleaned Data\n",
    "\n",
    "### Univariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "30d2fc2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The Total sales distributions')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Univariate analysis of column Total_Sales\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.distplot(df['Total_Sales'])\n",
    "plt.xlabel('The Total sales',fontsize=15)\n",
    "plt.ylabel('count',fontsize=15)\n",
    "plt.title('The Total sales distributions',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c5dd7a7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The Distribution of Temperature')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Univariate analysis of column Temperature\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.distplot(df['Temperature'])\n",
    "plt.xlabel('Temperature',fontsize=20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.title('The Distribution of Temperature',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "51dc639a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The Distribution of CPI')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Univariate analysis of column Temperature\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.distplot(df['CPI'])\n",
    "plt.xlabel('CPI',fontsize=20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.title('The Distribution of CPI',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e4356048",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Holidays')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(df['Holiday'])\n",
    "plt.xlabel('Holiday',fontsize=20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.title('Number of Holidays',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8e2e5c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Type')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(df['Type'])\n",
    "plt.xlabel('Type',fontsize=20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.title('Number of Type',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9a200a98",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weeknumber', ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(df.weeknumber)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4e52068",
   "metadata": {},
   "source": [
    "### Bivariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2893840e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Holidays')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Holidays and sales \n",
    "plt.figure(figsize=(8,8))\n",
    "sns.boxplot(x='Holiday',y='Total_Sales',data=df)\n",
    "plt.xlabel('Holiday',fontsize=20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.title('Number of Holidays',fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19727a11",
   "metadata": {},
   "source": [
    "By looking at above graph we conclude that there is more sales during the Holidays. It means that poeple prefer to do shopping during the holidays. So during the holidays we would recommended to give some good offer to attract the customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "08704c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Total_MarkDown')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  and sales \n",
    "plt.figure(figsize=(8,8))\n",
    "sns.boxplot(x='Total_MarkDown',y='Total_Sales',data=df)\n",
    "plt.xlabel('Total_MarkDown',fontsize=20)\n",
    "plt.ylabel('count',fontsize=20)\n",
    "plt.title('Number of Total_MarkDown',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6be68cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code for creating scatter plot for continuous variables \n",
    "plt.figure(figsize = (15,12))\n",
    "cols = [\"Temperature\", \"CPI\", \"Unemployment_Rate\", \"Size\",\"Fuel_Price\"]\n",
    "s = 1\n",
    "for r in range(len(cols)):\n",
    "  plt.subplot(2,3,s)\n",
    "  s = s + 1\n",
    "  sns.regplot(x = cols[r], y = 'Total_Sales', data = df, fit_reg=True)\n",
    "  plt.title(\"Total_Sales VS \" + cols[r], fontsize = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9280eb",
   "metadata": {},
   "source": [
    "### Features selection\n",
    "- Correlation\n",
    "- SelectKBest (ANOVA )\n",
    "- Features Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ef959a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continuous variables and categorical variables\n",
    "Cont_cols = df[[\"Temperature\", \"Fuel_Price\", \"CPI\", \"Unemployment_Rate\", \"Total_Sales\", \"Size\"]]\n",
    "cat_cols = df[[\"Holiday\", \"Type\",\"Total_MarkDown\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "99b4a994",
   "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>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.101416</td>\n",
       "      <td>0.158765</td>\n",
       "      <td>0.145653</td>\n",
       "      <td>-0.085423</td>\n",
       "      <td>-0.093383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Price</th>\n",
       "      <td>0.101416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.184169</td>\n",
       "      <td>-0.032907</td>\n",
       "      <td>-0.002804</td>\n",
       "      <td>0.015077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPI</th>\n",
       "      <td>0.158765</td>\n",
       "      <td>-0.184169</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302919</td>\n",
       "      <td>-0.070087</td>\n",
       "      <td>-0.008955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <td>0.145653</td>\n",
       "      <td>-0.032907</td>\n",
       "      <td>-0.302919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.107199</td>\n",
       "      <td>-0.073715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Sales</th>\n",
       "      <td>-0.085423</td>\n",
       "      <td>-0.002804</td>\n",
       "      <td>-0.070087</td>\n",
       "      <td>-0.107199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.795552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size</th>\n",
       "      <td>-0.093383</td>\n",
       "      <td>0.015077</td>\n",
       "      <td>-0.008955</td>\n",
       "      <td>-0.073715</td>\n",
       "      <td>0.795552</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Temperature  Fuel_Price       CPI  Unemployment_Rate  \\\n",
       "Temperature           1.000000    0.101416  0.158765           0.145653   \n",
       "Fuel_Price            0.101416    1.000000 -0.184169          -0.032907   \n",
       "CPI                   0.158765   -0.184169  1.000000          -0.302919   \n",
       "Unemployment_Rate     0.145653   -0.032907 -0.302919           1.000000   \n",
       "Total_Sales          -0.085423   -0.002804 -0.070087          -0.107199   \n",
       "Size                 -0.093383    0.015077 -0.008955          -0.073715   \n",
       "\n",
       "                   Total_Sales      Size  \n",
       "Temperature          -0.085423 -0.093383  \n",
       "Fuel_Price           -0.002804  0.015077  \n",
       "CPI                  -0.070087 -0.008955  \n",
       "Unemployment_Rate    -0.107199 -0.073715  \n",
       "Total_Sales           1.000000  0.795552  \n",
       "Size                  0.795552  1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cont_cols.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "06eb50ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(Cont_cols.corr(),annot = True, cmap = \"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c255a2c0",
   "metadata": {},
   "source": [
    "- sales vs Temeprature---> Weak correlation\n",
    "- sales vs fuel_price---> No correlation\n",
    "- sales vs CPI---> Weak correlation\n",
    "- sales vs unemployment_rate---> Weak correlation\n",
    "- sales vs size--->strong positive correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9114131e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing correlated features\n",
    "Threshold=0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7ccefb72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find and remove correlated features\n",
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  # Set of all the names of correlated columns\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n",
    "                colname = corr_matrix.columns[i]  # getting the name of column\n",
    "                col_corr.add(colname)\n",
    "    return col_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "796e1e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(Cont_cols,Threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83e474f",
   "metadata": {},
   "source": [
    "**From correlation we conclude that the none of independent features are highly correlated, so there is no multicollinarity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dd97276f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will remove fuel price \n",
    "df.drop('Fuel_Price',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0698fc1a",
   "metadata": {},
   "source": [
    "### Anova\n",
    "Since our dependent variable is continuous(\"Total_Sales\") and independents are both categorical as well as continuous so to \n",
    "\n",
    "- continous vs continous -----> correlation\n",
    "- categorical vs contnoius----> ANOVA\n",
    "\n",
    "In ANOVA test, we will find the p value and then we will try to reject or accept the null hypothesis to find whether the independent variable is correlated with the dependent variable or not.\n",
    "\n",
    "- Null: Independent and dependent variables are non-correlated.\n",
    "\n",
    "- Alternate: Independent and dependent variables are correlated.\n",
    "\n",
    "If p<0.05, we will reject null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "20faacc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anova test results for Holiday\n",
      "              df        sum_sq       mean_sq          F    PR(>F)\n",
      "Holiday      1.0  4.361665e+12  4.361665e+12  13.368263  0.000259\n",
      "Residual  3778.0  1.232649e+15  3.262701e+11        NaN       NaN\n",
      "################################################\n",
      "\n",
      "Anova test results for Type\n",
      "              df        sum_sq       mean_sq            F  PR(>F)\n",
      "Type         2.0  4.301251e+14  2.150626e+14  1006.700088     0.0\n",
      "Residual  3777.0  8.068851e+14  2.136312e+11          NaN     NaN\n",
      "################################################\n",
      "\n",
      "Anova test results for Total_MarkDown\n",
      "                    df        sum_sq       mean_sq         F   PR(>F)\n",
      "Total_MarkDown     1.0  1.035733e+12  1.035733e+12  3.165921  0.07527\n",
      "Residual        3778.0  1.235975e+15  3.271505e+11       NaN      NaN\n",
      "################################################\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ANOVA test\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "\n",
    "for col in cat_cols:\n",
    "    model = ols('Total_Sales' + '~' + col, data = df).fit() # Oridnary least square method\n",
    "    result_anova = sm.stats.anova_lm(model) # ANOVA Test\n",
    "    print(\"Anova test results for\", col)\n",
    "    print(result_anova)\n",
    "    print(\"################################################\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a80c6f4b",
   "metadata": {},
   "source": [
    "Since pvalue<0.05, we will not reject the null hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0514e0a4",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "- One hot encoding ----> nominal data\n",
    "- label encoding -----> ordinal data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1990bf20",
   "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>Temperature</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>weeknumber</th>\n",
       "      <th>Total_MarkDown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.31</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>17</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.51</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>48</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.93</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>7</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.63</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>8</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.50</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>18</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature         CPI  Unemployment_Rate Holiday  Total_Sales  \\\n",
       "0        42.31  211.096358              8.106      No   1643690.90   \n",
       "1        38.51  211.242170              8.106     Yes   1641957.44   \n",
       "2        39.93  211.289143              8.106      No   1611968.17   \n",
       "3        46.63  211.319643              8.106      No   1409727.59   \n",
       "4        46.50  211.350143              8.106      No          NaN   \n",
       "\n",
       "                     Type    Size  weeknumber Total_MarkDown  \n",
       "0  E-Commerce Fulfillment  151315          17            Low  \n",
       "1  E-Commerce Fulfillment  151315          48            Low  \n",
       "2  E-Commerce Fulfillment  151315           7            Low  \n",
       "3  E-Commerce Fulfillment  151315           8            Low  \n",
       "4  E-Commerce Fulfillment  151315          18            Low  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b462b9c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding \n",
    "df['Holiday'].replace({\"Yes\":1,\"No\":0},inplace=True)\n",
    "df['Total_MarkDown'].replace({\"High\" : 1, \"Low\" : 0}, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7135ff8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#One hot encoding\n",
    "Types= pd.get_dummies(df['Type'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ea293c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
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       "      <th>Food</th>\n",
       "      <th>Regional</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.31</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>E-Commerce Fulfillment</td>\n",
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       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.93</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.63</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.50</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Commerce Fulfillment</td>\n",
       "      <td>151315</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature         CPI  Unemployment_Rate  Holiday  Total_Sales  \\\n",
       "0        42.31  211.096358              8.106        0   1643690.90   \n",
       "1        38.51  211.242170              8.106        1   1641957.44   \n",
       "2        39.93  211.289143              8.106        0   1611968.17   \n",
       "3        46.63  211.319643              8.106        0   1409727.59   \n",
       "4        46.50  211.350143              8.106        0          NaN   \n",
       "\n",
       "                     Type    Size  weeknumber  Total_MarkDown  Food  Regional  \n",
       "0  E-Commerce Fulfillment  151315          17               0     0         0  \n",
       "1  E-Commerce Fulfillment  151315          48               0     0         0  \n",
       "2  E-Commerce Fulfillment  151315           7               0     0         0  \n",
       "3  E-Commerce Fulfillment  151315           8               0     0         0  \n",
       "4  E-Commerce Fulfillment  151315          18               0     0         0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we will concate the type \n",
    "df= pd.concat([df,Types],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "32a28183",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
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       "      <th>Regional</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.93</td>\n",
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       "      <td>8.106</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.63</td>\n",
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       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>151315</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.50</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151315</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Temperature         CPI  Unemployment_Rate  Holiday  Total_Sales    Size  \\\n",
       "0        42.31  211.096358              8.106        0   1643690.90  151315   \n",
       "1        38.51  211.242170              8.106        1   1641957.44  151315   \n",
       "2        39.93  211.289143              8.106        0   1611968.17  151315   \n",
       "3        46.63  211.319643              8.106        0   1409727.59  151315   \n",
       "4        46.50  211.350143              8.106        0          NaN  151315   \n",
       "\n",
       "   weeknumber  Total_MarkDown  Food  Regional  \n",
       "0          17               0     0         0  \n",
       "1          48               0     0         0  \n",
       "2           7               0     0         0  \n",
       "3           8               0     0         0  \n",
       "4          18               0     0         0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the column 'Type'\n",
    "df.drop('Type',inplace=True,axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04ddc6e",
   "metadata": {},
   "source": [
    "### Trianing Data\n",
    "We will seperate the train data and test data, we will predict the missing Total sales value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c5ea4bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training data\n",
    "train_df= df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "009a5374",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Temperature</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
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       "      <th>0</th>\n",
       "      <td>42.31</td>\n",
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       "      <td>8.106</td>\n",
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       "      <td>1643690.90</td>\n",
       "      <td>151315</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.51</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.93</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>151315</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.63</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>151315</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.58</td>\n",
       "      <td>211.215635</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1472515.79</td>\n",
       "      <td>151315</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature         CPI  Unemployment_Rate  Holiday  Total_Sales    Size  \\\n",
       "0        42.31  211.096358              8.106        0   1643690.90  151315   \n",
       "1        38.51  211.242170              8.106        1   1641957.44  151315   \n",
       "2        39.93  211.289143              8.106        0   1611968.17  151315   \n",
       "3        46.63  211.319643              8.106        0   1409727.59  151315   \n",
       "6        54.58  211.215635              8.106        0   1472515.79  151315   \n",
       "\n",
       "   weeknumber  Total_MarkDown  Food  Regional  \n",
       "0          17               0     0         0  \n",
       "1          48               0     0         0  \n",
       "2           7               0     0         0  \n",
       "3           8               0     0         0  \n",
       "6          11               0     0         0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping nan values from training data\n",
    "train_df.dropna(inplace=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "166d1df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3780, 10)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "076a683a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Independet and dependet variables\n",
    "X=train_df.drop('Total_Sales',axis=1)\n",
    "y= train_df['Total_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d10d5287",
   "metadata": {},
   "outputs": [
    {
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.63</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.58</td>\n",
       "      <td>211.215635</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature         CPI  Unemployment_Rate  Holiday    Size  weeknumber  \\\n",
       "0        42.31  211.096358              8.106        0  151315          17   \n",
       "1        38.51  211.242170              8.106        1  151315          48   \n",
       "2        39.93  211.289143              8.106        0  151315           7   \n",
       "3        46.63  211.319643              8.106        0  151315           8   \n",
       "6        54.58  211.215635              8.106        0  151315          11   \n",
       "\n",
       "   Total_MarkDown  Food  Regional  \n",
       "0               0     0         0  \n",
       "1               0     0         0  \n",
       "2               0     0         0  \n",
       "3               0     0         0  \n",
       "6               0     0         0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f3868f",
   "metadata": {},
   "source": [
    "### feature imporatance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fc1a11a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a484faca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(8).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd224e2",
   "metadata": {},
   "source": [
    "### Fitting models\n",
    "- Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "- Scaling of features\n",
    "- Import model\n",
    "- Fit the data\n",
    "- Predict w.r.t X_test\n",
    "- In regression check RSME Score\n",
    "- Plot error graph\n",
    "- finally pick the best model and predict the missing sales values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "920489e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a8e4f821",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### standarisation: We use the Standardscaler from sklearn library\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "###  fit_transform\n",
    "X_train_scaled=scaler.fit_transform(X_train) #alway give parameter in fit_transform\n",
    "X_test_scaled=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8e6971c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((756, 9), (3024, 9))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled.shape,X_train_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "013afaf4",
   "metadata": {},
   "source": [
    "### Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3eaf08a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg= LinearRegression()\n",
    "reg.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f05f7322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the model\n",
    "y_pred=reg.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "828b9994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7220341624925302\n",
      "0.6401731174398315\n"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "print(reg.score(X_test_scaled,y_test))\n",
    "print(reg.score(X_train_scaled,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355d8cb9",
   "metadata": {},
   "source": [
    "Here the Training and testing accuracy not too much differ, it means our model is not overfitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3672dac7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 224891.59628264653\n",
      "Mean Squared Error: 90470646254.37038\n",
      "Root Mean Squared Error: 300783.3875970719\n",
      "Mean Absolute percentage Error: 0.26108598877867656\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('Mean Absolute percentage Error:',metrics.mean_absolute_percentage_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c20729",
   "metadata": {},
   "source": [
    "### Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0ba80430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_reg= RandomForestRegressor()\n",
    "rf_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "05b317a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= rf_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f34db457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9654502172540046\n",
      "0.994364271676109\n"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "print(rf_reg.score(X_test,y_test))\n",
    "print(rf_reg.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f85948a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 58374.21558730162\n",
      "Mean Squared Error: 11245055151.406164\n",
      "Root Mean Squared Error: 106042.7043761435\n",
      "Mean Absolute percentage Error: 0.05707033647141502\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('Mean Absolute percentage Error:',metrics.mean_absolute_percentage_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515f74be",
   "metadata": {},
   "source": [
    "By random forest we achieve good accuracy which is 96.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c90369",
   "metadata": {},
   "source": [
    "### Stat Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f9ec9f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Total_Sales</td>   <th>  R-squared:         </th> <td>   0.640</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.639</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   595.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Oct 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:28:50</td>     <th>  Log-Likelihood:    </th> <td> -42833.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3024</td>      <th>  AIC:               </th> <td>8.569e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3014</td>      <th>  BIC:               </th> <td>8.575e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td> 2.329e+05</td> <td> 5.93e+04</td> <td>    3.925</td> <td> 0.000</td> <td> 1.17e+05</td> <td> 3.49e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Temperature</th>       <td>  353.7514</td> <td>  379.580</td> <td>    0.932</td> <td> 0.351</td> <td> -390.511</td> <td> 1098.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CPI</th>               <td>-1276.0361</td> <td>  172.218</td> <td>   -7.409</td> <td> 0.000</td> <td>-1613.713</td> <td> -938.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Unemployment_Rate</th> <td>-2.381e+04</td> <td> 3703.686</td> <td>   -6.430</td> <td> 0.000</td> <td>-3.11e+04</td> <td>-1.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Holiday</th>           <td> 6.603e+04</td> <td> 2.88e+04</td> <td>    2.295</td> <td> 0.022</td> <td> 9618.805</td> <td> 1.22e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Size</th>              <td>    7.9136</td> <td>    0.165</td> <td>   48.008</td> <td> 0.000</td> <td>    7.590</td> <td>    8.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weeknumber</th>        <td> 3791.2105</td> <td>  509.870</td> <td>    7.436</td> <td> 0.000</td> <td> 2791.482</td> <td> 4790.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total_MarkDown</th>    <td> 2.279e+04</td> <td> 1.34e+04</td> <td>    1.695</td> <td> 0.090</td> <td>-3573.802</td> <td> 4.92e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Food</th>              <td> 6.929e+04</td> <td> 1.86e+04</td> <td>    3.734</td> <td> 0.000</td> <td> 3.29e+04</td> <td> 1.06e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Regional</th>          <td> 1.998e+05</td> <td> 3.02e+04</td> <td>    6.620</td> <td> 0.000</td> <td> 1.41e+05</td> <td> 2.59e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>930.262</td> <th>  Durbin-Watson:     </th> <td>   2.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3943.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.442</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.794</td>  <th>  Cond. No.          </th> <td>1.43e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.43e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Total_Sales   R-squared:                       0.640\n",
       "Model:                            OLS   Adj. R-squared:                  0.639\n",
       "Method:                 Least Squares   F-statistic:                     595.8\n",
       "Date:                Thu, 28 Oct 2021   Prob (F-statistic):               0.00\n",
       "Time:                        18:28:50   Log-Likelihood:                -42833.\n",
       "No. Observations:                3024   AIC:                         8.569e+04\n",
       "Df Residuals:                    3014   BIC:                         8.575e+04\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const              2.329e+05   5.93e+04      3.925      0.000    1.17e+05    3.49e+05\n",
       "Temperature         353.7514    379.580      0.932      0.351    -390.511    1098.013\n",
       "CPI               -1276.0361    172.218     -7.409      0.000   -1613.713    -938.359\n",
       "Unemployment_Rate -2.381e+04   3703.686     -6.430      0.000   -3.11e+04   -1.66e+04\n",
       "Holiday            6.603e+04   2.88e+04      2.295      0.022    9618.805    1.22e+05\n",
       "Size                  7.9136      0.165     48.008      0.000       7.590       8.237\n",
       "weeknumber         3791.2105    509.870      7.436      0.000    2791.482    4790.939\n",
       "Total_MarkDown     2.279e+04   1.34e+04      1.695      0.090   -3573.802    4.92e+04\n",
       "Food               6.929e+04   1.86e+04      3.734      0.000    3.29e+04    1.06e+05\n",
       "Regional           1.998e+05   3.02e+04      6.620      0.000    1.41e+05    2.59e+05\n",
       "==============================================================================\n",
       "Omnibus:                      930.262   Durbin-Watson:                   2.029\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3943.649\n",
       "Skew:                           1.442   Prob(JB):                         0.00\n",
       "Kurtosis:                       7.794   Cond. No.                     1.43e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.43e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting stats \n",
    "# Adding intercept term to the data\n",
    "x_train = sm.add_constant(X_train)\n",
    "x_test = sm.add_constant(X_test)\n",
    "# training the model and printing the model statistics\n",
    "model_stats = sm.OLS(y_train,x_train).fit()\n",
    "model_stats.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a738753e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE after training the data by stats linear model: 90470646254.37021\n",
      "r2 score after training the data by stats linear model :  0.7220341624925307\n"
     ]
    }
   ],
   "source": [
    "# Performance on test data\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "y_pred_stat = model_stats.predict(x_test)\n",
    "\n",
    "test_MSE = mean_squared_error(y_test,y_pred_stat )\n",
    "test_r2score = r2_score(y_test,y_pred_stat )\n",
    "print(\"MSE after training the data by stats linear model:\",test_MSE)\n",
    "print(\"r2 score after training the data by stats linear model : \",test_r2score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "da35ba10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE after training the data by stats model: 117668522498.38525\n",
      "r2 score after training the data by stats model :  0.6401731174398315\n"
     ]
    }
   ],
   "source": [
    "# Performance on training data\n",
    "predict_train = model_stats.predict(x_train)\n",
    "\n",
    "train_MSE_ = mean_squared_error(y_train, predict_train)\n",
    "train_r2score= r2_score(y_train, predict_train)\n",
    "print(\"MSE after training the data by stats model:\",train_MSE_)\n",
    "print(\"r2 score after training the data by stats model : \",train_r2score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735d07ce",
   "metadata": {},
   "source": [
    "The accuracy of model in both sklearn model 72% and stat model is almost 64 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cfab1fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droppig the variable which is not statstically significant \n",
    "x_train_new=X_train.drop('Temperature',axis=1)\n",
    "x_test_new=X_test.drop('Temperature',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d9b5bd52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Again fitting the model on new train data\n",
    "reg.fit(x_train_new,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3f73ef85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Total_Sales</td>   <th>  R-squared:         </th> <td>   0.640</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.639</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   670.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Oct 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:28:51</td>     <th>  Log-Likelihood:    </th> <td> -42834.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3024</td>      <th>  AIC:               </th> <td>8.569e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3015</td>      <th>  BIC:               </th> <td>8.574e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td> 2.426e+05</td> <td> 5.84e+04</td> <td>    4.153</td> <td> 0.000</td> <td> 1.28e+05</td> <td> 3.57e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CPI</th>               <td>-1240.4188</td> <td>  167.921</td> <td>   -7.387</td> <td> 0.000</td> <td>-1569.669</td> <td> -911.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Unemployment_Rate</th> <td>-2.321e+04</td> <td> 3645.969</td> <td>   -6.365</td> <td> 0.000</td> <td>-3.04e+04</td> <td>-1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Holiday</th>           <td> 5.567e+04</td> <td> 2.65e+04</td> <td>    2.098</td> <td> 0.036</td> <td> 3640.796</td> <td> 1.08e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Size</th>              <td>    7.9002</td> <td>    0.164</td> <td>   48.110</td> <td> 0.000</td> <td>    7.578</td> <td>    8.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weeknumber</th>        <td> 3916.7257</td> <td>  491.751</td> <td>    7.965</td> <td> 0.000</td> <td> 2952.525</td> <td> 4880.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total_MarkDown</th>    <td> 2.334e+04</td> <td> 1.34e+04</td> <td>    1.737</td> <td> 0.082</td> <td>-3001.481</td> <td> 4.97e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Food</th>              <td>  6.74e+04</td> <td> 1.84e+04</td> <td>    3.654</td> <td> 0.000</td> <td> 3.12e+04</td> <td> 1.04e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Regional</th>          <td> 1.999e+05</td> <td> 3.02e+04</td> <td>    6.622</td> <td> 0.000</td> <td> 1.41e+05</td> <td> 2.59e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>920.884</td> <th>  Durbin-Watson:     </th> <td>   2.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3844.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.432</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.723</td>  <th>  Cond. No.          </th> <td>1.41e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.41e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Total_Sales   R-squared:                       0.640\n",
       "Model:                            OLS   Adj. R-squared:                  0.639\n",
       "Method:                 Least Squares   F-statistic:                     670.2\n",
       "Date:                Thu, 28 Oct 2021   Prob (F-statistic):               0.00\n",
       "Time:                        18:28:51   Log-Likelihood:                -42834.\n",
       "No. Observations:                3024   AIC:                         8.569e+04\n",
       "Df Residuals:                    3015   BIC:                         8.574e+04\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const              2.426e+05   5.84e+04      4.153      0.000    1.28e+05    3.57e+05\n",
       "CPI               -1240.4188    167.921     -7.387      0.000   -1569.669    -911.168\n",
       "Unemployment_Rate -2.321e+04   3645.969     -6.365      0.000   -3.04e+04   -1.61e+04\n",
       "Holiday            5.567e+04   2.65e+04      2.098      0.036    3640.796    1.08e+05\n",
       "Size                  7.9002      0.164     48.110      0.000       7.578       8.222\n",
       "weeknumber         3916.7257    491.751      7.965      0.000    2952.525    4880.926\n",
       "Total_MarkDown     2.334e+04   1.34e+04      1.737      0.082   -3001.481    4.97e+04\n",
       "Food                6.74e+04   1.84e+04      3.654      0.000    3.12e+04    1.04e+05\n",
       "Regional           1.999e+05   3.02e+04      6.622      0.000    1.41e+05    2.59e+05\n",
       "==============================================================================\n",
       "Omnibus:                      920.884   Durbin-Watson:                   2.029\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3844.045\n",
       "Skew:                           1.432   Prob(JB):                         0.00\n",
       "Kurtosis:                       7.723   Cond. No.                     1.41e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.41e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Building stat model\n",
    "x_train_stat = sm.add_constant(x_train_new)\n",
    "x_test_stat = sm.add_constant(x_test_new)\n",
    "# training the model and printing the model statistics\n",
    "model_stats = sm.OLS(y_train,x_train_stat).fit()\n",
    "model_stats.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "987eaa4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mse of stat model is: 90198341854.87434\n",
      "The r2 score of stat model is: 0.7228708020391303\n"
     ]
    }
   ],
   "source": [
    "# Performance on test data on stat model\n",
    "y_pred_test_stat= model_stats.predict(x_test_stat)\n",
    "\n",
    "print(\"The mse of stat model is:\", mean_squared_error(y_test,y_pred_test_stat))\n",
    "print(\"The r2 score of stat model is:\", r2_score(y_test,y_pred_test_stat))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b83348e",
   "metadata": {},
   "source": [
    "### Test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "21a2d7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Temperature</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Size</th>\n",
       "      <th>weeknumber</th>\n",
       "      <th>Total_MarkDown</th>\n",
       "      <th>Food</th>\n",
       "      <th>Regional</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46.50</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151315</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.79</td>\n",
       "      <td>211.380643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151315</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.27</td>\n",
       "      <td>210.820450</td>\n",
       "      <td>7.808</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151315</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.86</td>\n",
       "      <td>210.622857</td>\n",
       "      <td>7.808</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151315</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72.55</td>\n",
       "      <td>210.339968</td>\n",
       "      <td>7.808</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151315</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature         CPI  Unemployment_Rate  Holiday  Total_Sales    Size  \\\n",
       "0        46.50  211.350143              8.106        0          NaN  151315   \n",
       "1        57.79  211.380643              8.106        0          NaN  151315   \n",
       "2        62.27  210.820450              7.808        0          NaN  151315   \n",
       "3        65.86  210.622857              7.808        0          NaN  151315   \n",
       "4        72.55  210.339968              7.808        0          NaN  151315   \n",
       "\n",
       "   weeknumber  Total_MarkDown  Food  Regional  \n",
       "0          18               0     0         0  \n",
       "1          48               0     0         0  \n",
       "2           5               0     0         0  \n",
       "3          35               0     0         0  \n",
       "4          27               0     0         0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Test data to be predicted \n",
    "test_data = df[df['Total_Sales'].isna()].reset_index().drop(columns = ['index'])\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "984f65e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4408, 10), (3780, 10))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape, train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f2d85356",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the sales column from test data\n",
    "test_data.drop('Total_Sales',inplace=True,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "835e978e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4408, 9), (3780, 10))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape, train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f1d49c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scalling the test data\n",
    "test_data_scaled= scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0d72ca77",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.405118</td>\n",
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       "      <td>0.629452</td>\n",
       "      <td>0.837752</td>\n",
       "      <td>0.376557</td>\n",
       "      <td>0.0</td>\n",
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       "          0         1         2    3         4         5    6    7    8\n",
       "0  0.475147  0.842989  0.405118  0.0  0.630267  0.306122  0.0  0.0  0.0\n",
       "1  0.585616  0.843290  0.405118  0.0  0.630267  0.918367  0.0  0.0  0.0\n",
       "2  0.629452  0.837752  0.376557  0.0  0.630267  0.040816  0.0  0.0  0.0\n",
       "3  0.664579  0.835799  0.376557  0.0  0.630267  0.653061  0.0  0.0  0.0\n",
       "4  0.730039  0.833002  0.376557  0.0  0.630267  0.489796  0.0  0.0  0.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting into data frame\n",
    "test_data_scaled=pd.DataFrame(test_data_scaled)\n",
    "test_data_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a9c7e546",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction on test data and saving of results\n",
    "prediction_result = rf_reg.predict(test_data_scaled)\n",
    "test_data_scaled['Total_Sales'] = prediction_result\n",
    "test_data_scaled.to_csv(\"predicted_total_sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5b336282",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv('Actucal_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c42a4dc",
   "metadata": {},
   "source": [
    "### Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80b5d0d",
   "metadata": {},
   "source": [
    "In this notebook, we tried to predict the total sales of sales history dataof store which is located on different location. We have build both sklearn and stat model , from sklearn model we got accuracy of 96.6% and from satat model we achieved 64%. We have done:\n",
    "- Data preprocessing\n",
    "- Univariate/Bi-variate analysis\n",
    "- Feature selection\n",
    "- Feature engineering\n",
    "- Model Building\n",
    "- stat model building\n",
    "- prediction of test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94c8fc85",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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