diff --git a/Week_8/Sample_Submission.xlsx b/Week_8/Sample_Submission.xlsx
new file mode 100644
index 0000000..ae136d6
Binary files /dev/null and b/Week_8/Sample_Submission.xlsx differ
diff --git a/Week_8/Test.csv b/Week_8/Test.csv
new file mode 100644
index 0000000..8352f21
--- /dev/null
+++ b/Week_8/Test.csv
@@ -0,0 +1,1052 @@
+Product,Product_Brand,Item_Category,Subcategory_1,Subcategory_2,Item_Rating,Date
+P-11284,B-2984,computers,network components,routers,4.3,1/12/2018
+P-6580,B-1732,jewellery,bangles bracelets armlets,bracelets,3.0,20/12/2012
+P-5843,B-3078,clothing,women s clothing,western wear,1.5,1/12/2014
+P-5334,B-1421,jewellery,necklaces chains,necklaces,3.9,1/12/2019
+P-5586,B-3078,clothing,women s clothing,western wear,1.4,1/12/2017
+P-11206,B-3078,clothing,women s clothing,western wear,2.1,3/12/2018
+P-9966,B-2609,mobiles accessories,mobile accessories,speakers,2.8,20/5/2015
+P-5367,B-1431,jewellery,rings,unknown,4.9,7/1/2016
+P-9075,B-2326,jewellery,rings,unknown,4.9,1/1/2015
+P-8852,B-449,clothing,women s clothing,lingerie sleep swimwear,4.5,7/1/2018
+P-8545,B-2250,computers,laptop accessories,computer accessories combos,4.8,3/3/2016
+P-6041,B-1586,home entertainment,video players accessories,video accessories,3.6,20/3/2013
+P-12536,B-3346,automotive,accessories spare parts,car interior exterior,1.8,25/3/2019
+P-9869,B-3078,footwear,men s footwear,formal shoes,2.0,29/12/2017
+P-11800,B-3078,beauty and personal care,fragrances,deodorants,4.5,1/12/2014
+P-1934,B-410,mobiles accessories,tablet accessories,cases covers,3.4,7/1/2017
+P-8186,B-2166,jewellery,earrings,unknown,1.0,12/12/2018
+P-7813,B-2077,olvin rectangular sunglasses,unknown,unknown,4.5,10/5/2014
+P-75,B-11,jewellery,bangles bracelets armlets,bracelets,3.6,20/12/2019
+P-7184,B-3078,clothing,men s clothing,winter seasonal wear,2.7,7/1/2014
+P-6328,B-3078,clothing,women s clothing,western wear,3.7,1/12/2017
+P-3049,B-786,jewellery,necklaces chains,necklaces,1.7,1/12/2017
+P-5913,B-2372,clothing,men s clothing,shirts,3.7,6/12/2014
+P-4460,B-1176,computers,laptop accessories,laptop adapters,2.7,10/6/2016
+P-4290,B-2281,clothing,women s clothing,lingerie sleep swimwear,2.1,7/1/2019
+P-6911,B-3078,clothing,women s clothing,western wear,1.8,1/12/2015
+P-5193,B-2372,clothing,men s clothing,shirts,3.0,6/12/2015
+P-5746,B-1517,jewellery,rings,unknown,1.8,1/1/2013
+P-6074,B-2372,clothing,men s clothing,shirts,4.8,6/12/2012
+P-2925,B-3078,toys school supplies,school supplies,geometry pencil boxes,3.7,1/12/2012
+P-8546,B-2250,computers,laptop accessories,computer accessories combos,2.9,3/3/2019
+P-173,B-42,clothing,kids clothing,girls wear,1.1,11/3/2015
+P-1270,B-3078,footwear,men s footwear,casual shoes,2.6,29/12/2016
+P-2844,B-735,jewellery,necklaces chains,necklaces,1.3,1/12/2019
+P-11688,B-3137,jewellery,bangles bracelets armlets,bangles,3.9,30/12/2019
+P-1176,B-3078,beauty and personal care,fragrances,deodorants,2.2,1/12/2012
+P-2049,B-3078,footwear,women s footwear,casual shoes,4.8,4/12/2012
+P-4613,B-1222,automotive,accessories spare parts,car interior exterior,3.1,7/3/2015
+P-3108,B-801,beauty and personal care,makeup,eyes,1.2,3/3/2013
+P-7087,B-3078,clothing,women s clothing,western wear,3.2,30/12/2018
+P-7835,B-3078,clothing,women s clothing,western wear,3.3,7/1/2014
+P-11064,B-2933,mobiles accessories,tablet accessories,cases covers,3.9,20/12/2018
+P-9742,B-3078,footwear,women s footwear,sports shoes,1.0,20/12/2017
+P-7211,B-1933,jewellery,bangles bracelets armlets,bracelets,3.1,20/12/2017
+P-4549,B-1204,clothing,women s clothing,lingerie sleep swimwear,3.8,15/6/2020
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+P-6874,B-3078,watches,wrist watches,maxima wrist watches,5.0,7/1/2015
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+P-5138,B-1363,computers,network components,routers,4.0,1/12/2012
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+P-9594,B-2492,clothing,women s clothing,ethnic wear,4.3,24/6/2019
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+P-11469,B-3062,eyewear,frames,ultravision full rim round frame 51 mm,2.2,26/6/2020
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+P-10404,B-2741,speedwav 216456 manual rear view mirror right,unknown,unknown,2.9,3/3/2014
+P-12199,B-3251,jewellery,necklaces chains,necklaces,3.9,1/12/2012
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+P-2218,B-506,clothing,kids clothing,boys wear,2.0,18/3/2017
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+P-50,B-3,baby care,baby bath skin,baby shower caps,1.4,16/6/2014
+P-11740,B-3153,automotive,accessories spare parts,car bike care,1.7,25/2/2018
+P-9288,B-2400,clothing,women s clothing,fusion wear,2.1,10/4/2013
+P-7048,B-3078,kitchen dining,containers bottles,bottles sippers,3.8,3/12/2015
+P-1678,B-3078,pens stationery,school supplies,geometry pen pencil boxes,3.6,1/12/2012
+P-5216,B-1384,home furnishing,bed linen,bedsheets,3.3,20/6/2016
+P-7275,B-3078,clothing,women s clothing,western wear,3.9,1/12/2013
+P-5980,B-1569,mobiles accessories,mobile accessories,mobile pouches,4.0,5/3/2018
+P-5410,B-1444,cameras accessories,camera accessories,lens hoods,1.4,29/12/2018
+P-8168,B-3078,clothing,women s clothing,ethnic wear,1.2,12/12/2015
+P-9670,B-2515,tools hardware,tools,tool accessories,4.2,29/12/2012
+P-3356,B-3078,clothing,women s clothing,ethnic wear,4.0,31/12/2019
+P-3175,B-819,mobiles accessories,mobile accessories,mobile body panels,3.0,25/2/2015
+P-12521,B-3345,computers,network components,routers,2.1,1/12/2013
+P-11887,B-3197,jewellery,bangles bracelets armlets,bracelets,2.5,4/1/2018
+P-1861,B-405,jewellery,rings,unknown,2.2,1/1/2016
+P-8989,B-2307,jewellery,rings,unknown,2.3,1/1/2014
+P-6286,B-1645,computers,laptop accessories,batteries,2.6,7/1/2018
+P-12089,B-3223,bags wallets belts,bags,backpacks,2.6,24/5/2013
+P-1835,B-405,jewellery,rings,unknown,4.6,1/1/2016
+P-6363,B-3078,clothing,men s clothing,shirts,3.4,6/12/2016
+P-11104,B-449,clothing,women s clothing,lingerie sleep swimwear,1.5,7/1/2016
+P-10210,B-2706,automotive,accessories spare parts,car electronics accessories,4.0,1/12/2018
+P-4034,B-1040,jewellery,rings,unknown,2.8,6/1/2014
+P-11586,B-3099,jewellery,jewellery sets,unknown,4.4,31/12/2016
+P-9667,B-2515,tools hardware,tools,tool accessories,4.2,29/12/2016
+P-10820,B-2679,clothing,kids clothing,boys wear,4.0,12/12/2019
+P-3988,B-1024,baby care,feeding nursing,utensils,4.4,12/12/2018
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+P-11876,B-3078,bags wallets belts,bags,sling bags,3.0,12/12/2013
+P-10171,B-2696,computers,network components,routers,3.5,1/12/2012
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+P-1297,B-216,clothing,kids clothing,boys wear,3.0,12/5/2019
+P-227,B-64,automotive,accessories spare parts,car interior exterior,5.0,1/12/2015
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+P-12032,B-3213,baby care,baby kids gifts,stickers,3.8,1/12/2012
+P-5938,B-1564,automotive,car bike accessories,car interior exterior,3.2,17/6/2015
+P-7219,B-3078,clothing,men s clothing,winter seasonal wear,2.1,29/12/2018
+P-7814,B-2077,olvin wayfarer sunglasses,unknown,unknown,1.7,10/5/2020
+P-11993,B-3199,jewellery,necklaces chains,necklaces,4.8,1/12/2019
+P-4130,B-1070,jewellery,pendants lockets,gehna yellow gold zircon silver pendant,2.7,22/5/2018
+P-7560,B-2372,clothing,women s clothing,western wear,3.2,30/12/2016
+P-11013,B-2923,jewellery,earrings,unknown,1.6,12/12/2019
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+P-5026,B-2679,clothing,women s clothing,western wear,1.7,30/12/2012
+P-9032,B-2312,clothing,men s clothing,winter seasonal wear,4.3,30/5/2017
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+P-2186,B-502,mobiles accessories,mobile accessories,mobile body panels,4.3,20/6/2015
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+P-8407,B-2211,kitchen dining,housekeeping laundry,home cleaning,5.0,12/12/2018
+P-3869,B-994,footwear,women s footwear,flats,3.0,21/4/2014
+P-5278,B-1408,bags wallets belts,bags,hand bags,2.1,5/6/2016
+P-11665,B-3131,jewellery,jewellery sets,unknown,4.4,12/12/2017
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diff --git a/Week_8/Train.csv b/Week_8/Train.csv
new file mode 100644
index 0000000..a5f654b
--- /dev/null
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diff --git a/Week_8/Week_8_Rank_2_Mrutyunjaya_Rath.ipynb b/Week_8/Week_8_Rank_2_Mrutyunjaya_Rath.ipynb
new file mode 100644
index 0000000..95de3e4
--- /dev/null
+++ b/Week_8/Week_8_Rank_2_Mrutyunjaya_Rath.ipynb
@@ -0,0 +1,9226 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.3"
+    },
+    "colab": {
+      "name": "Rank_2_Mrutyunjaya_Rath.ipynb",
+      "provenance": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/serendipity-sd/MachineHack/blob/master/Week_8_Rank_2_Mrutyunjaya_Rath.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XvN_GRSnXTxh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "9b39c18f-a63f-4694-b51b-520f0fa68bf4"
+      },
+      "source": [
+        "import os \n",
+        "from google.colab import drive\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "drive.mount(\"/content/gdrive/\", force_remount=True)\n",
+        "os.chdir('gdrive/')\n"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mounted at /content/gdrive/\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AbbqDeW1Xp_Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "os.chdir('My Drive')"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Y9SmyYetXuHe",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "os.chdir(\"MachineHack_Hackathon_Solutions_Ecommerce_Price_Prediction_Weekend_Hackathon_8\")"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "n9Q_nXF1XRJ4",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "1531a5df-c51b-47ef-e820-3c37f8d5b584"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import os\n",
+        "os.getcwd()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'/content/gdrive/My Drive/MachineHack_Hackathon_Solutions_Ecommerce_Price_Prediction_Weekend_Hackathon_8'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "P4QCIbLtXRJ_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "train=pd.read_csv('Train.csv')\n",
+        "test=pd.read_csv('Test.csv')\n",
+        "submission=pd.read_excel('sub.xlsx')"
+      ],
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "c1ApPeOgXRKE",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "029dc19c-75d2-4e14-c08f-1766a8bb4ba7"
+      },
+      "source": [
+        "train.head()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Product</th>\n",
+              "      <th>Product_Brand</th>\n",
+              "      <th>Item_Category</th>\n",
+              "      <th>Subcategory_1</th>\n",
+              "      <th>Subcategory_2</th>\n",
+              "      <th>Item_Rating</th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Selling_Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>P-2610</td>\n",
+              "      <td>B-659</td>\n",
+              "      <td>bags wallets belts</td>\n",
+              "      <td>bags</td>\n",
+              "      <td>hand bags</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>2/3/2017</td>\n",
+              "      <td>291.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>P-2453</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>women s clothing</td>\n",
+              "      <td>western wear</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>7/1/2015</td>\n",
+              "      <td>897.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>P-6802</td>\n",
+              "      <td>B-1810</td>\n",
+              "      <td>home decor festive needs</td>\n",
+              "      <td>showpieces</td>\n",
+              "      <td>ethnic</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1/12/2019</td>\n",
+              "      <td>792.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>P-4452</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>beauty and personal care</td>\n",
+              "      <td>eye care</td>\n",
+              "      <td>h2o plus eye care</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>12/12/2014</td>\n",
+              "      <td>837.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>P-8454</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>men s clothing</td>\n",
+              "      <td>t shirts</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>12/12/2013</td>\n",
+              "      <td>470.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Product Product_Brand  ...        Date Selling_Price\n",
+              "0  P-2610         B-659  ...    2/3/2017         291.0\n",
+              "1  P-2453        B-3078  ...    7/1/2015         897.0\n",
+              "2  P-6802        B-1810  ...   1/12/2019         792.0\n",
+              "3  P-4452        B-3078  ...  12/12/2014         837.0\n",
+              "4  P-8454        B-3078  ...  12/12/2013         470.0\n",
+              "\n",
+              "[5 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EBxoU5XWXRKI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "35d43202-2d41-4326-fee4-832e687b4024"
+      },
+      "source": [
+        "test.head()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Product</th>\n",
+              "      <th>Product_Brand</th>\n",
+              "      <th>Item_Category</th>\n",
+              "      <th>Subcategory_1</th>\n",
+              "      <th>Subcategory_2</th>\n",
+              "      <th>Item_Rating</th>\n",
+              "      <th>Date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>P-11284</td>\n",
+              "      <td>B-2984</td>\n",
+              "      <td>computers</td>\n",
+              "      <td>network components</td>\n",
+              "      <td>routers</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>1/12/2018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>P-6580</td>\n",
+              "      <td>B-1732</td>\n",
+              "      <td>jewellery</td>\n",
+              "      <td>bangles bracelets armlets</td>\n",
+              "      <td>bracelets</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>20/12/2012</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>P-5843</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>women s clothing</td>\n",
+              "      <td>western wear</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>1/12/2014</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>P-5334</td>\n",
+              "      <td>B-1421</td>\n",
+              "      <td>jewellery</td>\n",
+              "      <td>necklaces chains</td>\n",
+              "      <td>necklaces</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>1/12/2019</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>P-5586</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>women s clothing</td>\n",
+              "      <td>western wear</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>1/12/2017</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Product Product_Brand Item_Category  ... Subcategory_2 Item_Rating        Date\n",
+              "0  P-11284        B-2984     computers  ...       routers         4.3   1/12/2018\n",
+              "1   P-6580        B-1732     jewellery  ...     bracelets         3.0  20/12/2012\n",
+              "2   P-5843        B-3078      clothing  ...  western wear         1.5   1/12/2014\n",
+              "3   P-5334        B-1421     jewellery  ...     necklaces         3.9   1/12/2019\n",
+              "4   P-5586        B-3078      clothing  ...  western wear         1.4   1/12/2017\n",
+              "\n",
+              "[5 rows x 7 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FXvogZdWXRKM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "e7aa0ef6-fc21-45ae-bd12-0e781c267ab2"
+      },
+      "source": [
+        "submission.head()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Selling_Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2985.101132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>542.891234</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>712.253540</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>340.579204</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>676.768173</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Selling_Price\n",
+              "0    2985.101132\n",
+              "1     542.891234\n",
+              "2     712.253540\n",
+              "3     340.579204\n",
+              "4     676.768173"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ed70SpPQXRKR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "outputId": "e68af120-269c-45d1-eda7-10d779d0af15"
+      },
+      "source": [
+        "sns.distplot(train['Selling_Price'])"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c38f87828>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FR_sLdCWXRKV",
+        "colab_type": "text"
+      },
+      "source": [
+        "### The Target Variable is highly skewed. So we are going to normalize it by applying log transformation through np.log1p"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FrSCj5kHXRKW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "train['Selling_Price']=np.log1p(train['Selling_Price'])"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G4ZZgV59XRKa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "30b4586d-460d-4d0d-d4da-0bcefebabcc8"
+      },
+      "source": [
+        "sns.distplot(train['Selling_Price'])"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c38e6c8d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DduKm0DDXRKg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "5da21f2a-7b13-4d0e-ac5c-600ce075ca58"
+      },
+      "source": [
+        "train.describe()"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Item_Rating</th>\n",
+              "      <th>Selling_Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2452.000000</td>\n",
+              "      <td>2452.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>3.078467</td>\n",
+              "      <td>6.652575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.187137</td>\n",
+              "      <td>1.177109</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.526361</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>5.918894</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.100000</td>\n",
+              "      <td>6.391917</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>4.100000</td>\n",
+              "      <td>7.086947</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>11.663842</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       Item_Rating  Selling_Price\n",
+              "count  2452.000000    2452.000000\n",
+              "mean      3.078467       6.652575\n",
+              "std       1.187137       1.177109\n",
+              "min       1.000000       3.526361\n",
+              "25%       2.000000       5.918894\n",
+              "50%       3.100000       6.391917\n",
+              "75%       4.100000       7.086947\n",
+              "max       5.000000      11.663842"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AYyZlEMUXRKj",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "outputId": "67cee06f-854e-41af-90cf-d49ec69f37f8"
+      },
+      "source": [
+        "train.info()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 2452 entries, 0 to 2451\n",
+            "Data columns (total 8 columns):\n",
+            " #   Column         Non-Null Count  Dtype  \n",
+            "---  ------         --------------  -----  \n",
+            " 0   Product        2452 non-null   object \n",
+            " 1   Product_Brand  2452 non-null   object \n",
+            " 2   Item_Category  2452 non-null   object \n",
+            " 3   Subcategory_1  2452 non-null   object \n",
+            " 4   Subcategory_2  2452 non-null   object \n",
+            " 5   Item_Rating    2452 non-null   float64\n",
+            " 6   Date           2452 non-null   object \n",
+            " 7   Selling_Price  2452 non-null   float64\n",
+            "dtypes: float64(2), object(6)\n",
+            "memory usage: 153.4+ KB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lU_dKdefXRKn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 168
+        },
+        "outputId": "0374d492-e5da-44bb-bb60-f5ad42e660a3"
+      },
+      "source": [
+        "train.nunique()"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Product          2452\n",
+              "Product_Brand    1010\n",
+              "Item_Category      75\n",
+              "Subcategory_1     130\n",
+              "Subcategory_2     336\n",
+              "Item_Rating        41\n",
+              "Date              483\n",
+              "Selling_Price    1095\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WI21RCBqXRKr",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "a5248c45-eca7-490c-8e54-089877a62651"
+      },
+      "source": [
+        "train.shape"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(2452, 8)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IA-yf9b5XRKv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "ae35dbc8-3e37-42d7-9a63-32100de97495"
+      },
+      "source": [
+        "train[(train['Product_Brand']=='B-3078')]['Item_Category'].nunique()"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "13"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dxOkabDfXRKy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Extracting Date features \n",
+        "def extract_time_features(df):\n",
+        "    df['Date']=pd.to_datetime(df['Date'])\n",
+        "    #df['year'] = df['Date'].dt.year\n",
+        "    df['month'] = df['Date'].dt.month\n",
+        "    #df['day_of_week'] = df['Date'].dt.dayofweek\n",
+        "    df['Day'] = df['Date'].dt.day\n",
+        "    df['DayOfyear'] = df['Date'].dt.dayofyear\n",
+        "    df['Week'] = pd.to_datetime(df['Date']).dt.week \n",
+        "    df['Quarter'] = pd.to_datetime(df['Date']).dt.quarter  \n",
+        "    df['Is_month_start'] = pd.to_datetime(df['Date']).dt.is_month_start \n",
+        "    df['Is_month_end'] = pd.to_datetime(df['Date']).dt.is_month_end \n",
+        "    del df['Date']\n",
+        "    return df"
+      ],
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ul9Nskf7XRK2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "train=extract_time_features(train)\n",
+        "test=extract_time_features(test)"
+      ],
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pUB3RoHFXRK5",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "train['Unique_Item_category_per_product_brand']=train.groupby(['Product_Brand'])['Item_Category'].transform('nunique')\n",
+        "train['Unique_Subcategory_1_per_product_brand']=train.groupby(['Product_Brand'])['Subcategory_1'].transform('nunique')\n",
+        "train['Unique_Subcategory_2_per_product_brand']=train.groupby(['Product_Brand'])['Subcategory_2'].transform('nunique')"
+      ],
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lnOlebdvUyna",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "dfd2a4c4-ae7a-4879-d922-b8417fd8bf08"
+      },
+      "source": [
+        "train.shape"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(2452, 17)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PpcLXk8rXRK8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "test['Unique_Item_category_per_product_brand']=test.groupby(['Product_Brand'])['Item_Category'].transform('nunique')\n",
+        "test['Unique_Subcategory_1_per_product_brand']=test.groupby(['Product_Brand'])['Subcategory_1'].transform('nunique')\n",
+        "test['Unique_Subcategory_2_per_product_brand']=test.groupby(['Product_Brand'])['Subcategory_2'].transform('nunique')"
+      ],
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Je4L0CCUUyB5",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "95bdb455-914e-4916-a9a4-e3fe09a14ed9"
+      },
+      "source": [
+        "train.shape"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(2452, 17)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zTGzB7DlXRLA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "0f8e247a-59a4-4a40-d116-30d05cb15dc3"
+      },
+      "source": [
+        "train.head()"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Product</th>\n",
+              "      <th>Product_Brand</th>\n",
+              "      <th>Item_Category</th>\n",
+              "      <th>Subcategory_1</th>\n",
+              "      <th>Subcategory_2</th>\n",
+              "      <th>Item_Rating</th>\n",
+              "      <th>Selling_Price</th>\n",
+              "      <th>month</th>\n",
+              "      <th>Day</th>\n",
+              "      <th>DayOfyear</th>\n",
+              "      <th>Week</th>\n",
+              "      <th>Quarter</th>\n",
+              "      <th>Is_month_start</th>\n",
+              "      <th>Is_month_end</th>\n",
+              "      <th>Unique_Item_category_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_1_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_2_per_product_brand</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>P-2610</td>\n",
+              "      <td>B-659</td>\n",
+              "      <td>bags wallets belts</td>\n",
+              "      <td>bags</td>\n",
+              "      <td>hand bags</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>5.676754</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>34</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>P-2453</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>women s clothing</td>\n",
+              "      <td>western wear</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>6.800170</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>182</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>P-6802</td>\n",
+              "      <td>B-1810</td>\n",
+              "      <td>home decor festive needs</td>\n",
+              "      <td>showpieces</td>\n",
+              "      <td>ethnic</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>6.675823</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>P-4452</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>beauty and personal care</td>\n",
+              "      <td>eye care</td>\n",
+              "      <td>h2o plus eye care</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.731018</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>P-8454</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>men s clothing</td>\n",
+              "      <td>t shirts</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>6.154858</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Product  ... Unique_Subcategory_2_per_product_brand\n",
+              "0  P-2610  ...                                      1\n",
+              "1  P-2453  ...                                    100\n",
+              "2  P-6802  ...                                      1\n",
+              "3  P-4452  ...                                    100\n",
+              "4  P-8454  ...                                    100\n",
+              "\n",
+              "[5 rows x 17 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mNWAeAj0XRLD",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "T4-1dYvYXRLG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# We will concat both train and test data set to apply some feature engineering like Label Encoder\n",
+        "total=pd.concat([train,test],0)"
+      ],
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9rryJ7qOXRLK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "f80d41af-abbc-4b70-c0b1-ea6cfe4ef3bd"
+      },
+      "source": [
+        "total.head()"
+      ],
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Product</th>\n",
+              "      <th>Product_Brand</th>\n",
+              "      <th>Item_Category</th>\n",
+              "      <th>Subcategory_1</th>\n",
+              "      <th>Subcategory_2</th>\n",
+              "      <th>Item_Rating</th>\n",
+              "      <th>Selling_Price</th>\n",
+              "      <th>month</th>\n",
+              "      <th>Day</th>\n",
+              "      <th>DayOfyear</th>\n",
+              "      <th>Week</th>\n",
+              "      <th>Quarter</th>\n",
+              "      <th>Is_month_start</th>\n",
+              "      <th>Is_month_end</th>\n",
+              "      <th>Unique_Item_category_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_1_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_2_per_product_brand</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>P-2610</td>\n",
+              "      <td>B-659</td>\n",
+              "      <td>bags wallets belts</td>\n",
+              "      <td>bags</td>\n",
+              "      <td>hand bags</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>5.676754</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>34</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>P-2453</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>women s clothing</td>\n",
+              "      <td>western wear</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>6.800170</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>182</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>P-6802</td>\n",
+              "      <td>B-1810</td>\n",
+              "      <td>home decor festive needs</td>\n",
+              "      <td>showpieces</td>\n",
+              "      <td>ethnic</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>6.675823</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>P-4452</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>beauty and personal care</td>\n",
+              "      <td>eye care</td>\n",
+              "      <td>h2o plus eye care</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.731018</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>P-8454</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>men s clothing</td>\n",
+              "      <td>t shirts</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>6.154858</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Product  ... Unique_Subcategory_2_per_product_brand\n",
+              "0  P-2610  ...                                      1\n",
+              "1  P-2453  ...                                    100\n",
+              "2  P-6802  ...                                      1\n",
+              "3  P-4452  ...                                    100\n",
+              "4  P-8454  ...                                    100\n",
+              "\n",
+              "[5 rows x 17 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LpbHC144XRLN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# total['average_rating_per_product_brand']=total.groupby(['Product_Brand'])['Item_Rating'].transform('mean')\n",
+        "# total['average_rating_Item_Category']=total.groupby(['Item_Category'])['Item_Rating'].transform('mean')\n",
+        "# total['average_rating_Subcategory_1']=total.groupby(['Subcategory_1'])['Item_Rating'].transform('mean')\n",
+        "# total['average_rating_Subcategory_2']=total.groupby(['Subcategory_2'])['Item_Rating'].transform('mean')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qIAYosdBXRLR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "total['std_rating_per_product_brand']=total.groupby(['Product_Brand'])['Item_Rating'].transform('std')\n",
+        "total['std_rating_Item_Category']=total.groupby(['Item_Category'])['Item_Rating'].transform('std')\n",
+        "total['std_rating_Subcategory_1']=total.groupby(['Subcategory_1'])['Item_Rating'].transform('std')\n",
+        "total['std_rating_Subcategory_2']=total.groupby(['Subcategory_2'])['Item_Rating'].transform('std')"
+      ],
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "v5u-GXjtWDHe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "6b24eb91-4213-4745-8e5b-a3a5f723176c"
+      },
+      "source": [
+        "total.head()"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Product</th>\n",
+              "      <th>Product_Brand</th>\n",
+              "      <th>Item_Category</th>\n",
+              "      <th>Subcategory_1</th>\n",
+              "      <th>Subcategory_2</th>\n",
+              "      <th>Item_Rating</th>\n",
+              "      <th>Selling_Price</th>\n",
+              "      <th>month</th>\n",
+              "      <th>Day</th>\n",
+              "      <th>DayOfyear</th>\n",
+              "      <th>Week</th>\n",
+              "      <th>Quarter</th>\n",
+              "      <th>Is_month_start</th>\n",
+              "      <th>Is_month_end</th>\n",
+              "      <th>Unique_Item_category_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_1_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_2_per_product_brand</th>\n",
+              "      <th>std_rating_per_product_brand</th>\n",
+              "      <th>std_rating_Item_Category</th>\n",
+              "      <th>std_rating_Subcategory_1</th>\n",
+              "      <th>std_rating_Subcategory_2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>P-2610</td>\n",
+              "      <td>B-659</td>\n",
+              "      <td>bags wallets belts</td>\n",
+              "      <td>bags</td>\n",
+              "      <td>hand bags</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>5.676754</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>34</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.105474</td>\n",
+              "      <td>1.094790</td>\n",
+              "      <td>1.018614</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>P-2453</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>women s clothing</td>\n",
+              "      <td>western wear</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>6.800170</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>182</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "      <td>1.197104</td>\n",
+              "      <td>1.205383</td>\n",
+              "      <td>1.227087</td>\n",
+              "      <td>1.186726</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>P-6802</td>\n",
+              "      <td>B-1810</td>\n",
+              "      <td>home decor festive needs</td>\n",
+              "      <td>showpieces</td>\n",
+              "      <td>ethnic</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>6.675823</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.202082</td>\n",
+              "      <td>1.192523</td>\n",
+              "      <td>1.094301</td>\n",
+              "      <td>1.028398</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>P-4452</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>beauty and personal care</td>\n",
+              "      <td>eye care</td>\n",
+              "      <td>h2o plus eye care</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.731018</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "      <td>1.197104</td>\n",
+              "      <td>1.215595</td>\n",
+              "      <td>0.636396</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>P-8454</td>\n",
+              "      <td>B-3078</td>\n",
+              "      <td>clothing</td>\n",
+              "      <td>men s clothing</td>\n",
+              "      <td>t shirts</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>6.154858</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "      <td>1.197104</td>\n",
+              "      <td>1.205383</td>\n",
+              "      <td>1.146774</td>\n",
+              "      <td>1.123001</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Product Product_Brand  ... std_rating_Subcategory_1 std_rating_Subcategory_2\n",
+              "0  P-2610         B-659  ...                 1.094790                 1.018614\n",
+              "1  P-2453        B-3078  ...                 1.227087                 1.186726\n",
+              "2  P-6802        B-1810  ...                 1.094301                 1.028398\n",
+              "3  P-4452        B-3078  ...                 0.636396                      NaN\n",
+              "4  P-8454        B-3078  ...                 1.146774                 1.123001\n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tFcN3M3LXRLU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# total['var_rating_per_product_brand']=total.groupby(['Product_Brand'])['Item_Rating'].transform('var')\n",
+        "# total['var_rating_Item_Category']=total.groupby(['Item_Category'])['Item_Rating'].transform('var')\n",
+        "# total['var_rating_Subcategory_1']=total.groupby(['Subcategory_1'])['Item_Rating'].transform('var')\n",
+        "# total['var_rating_Subcategory_2']=total.groupby(['Subcategory_2'])['Item_Rating'].transform('var')"
+      ],
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6_Wjfm-5XRLY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "total['Product_Brand']=total['Product_Brand'].str.lstrip('B-').astype(int)"
+      ],
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-DWmbHFIXRLc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "le=LabelEncoder()"
+      ],
+      "execution_count": 31,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZolijN2GXRLg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "cat=['Item_Category','Subcategory_1','Subcategory_2']\n",
+        "for items in cat:\n",
+        "    total[items]=le.fit_transform(total[items])"
+      ],
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eTEz7j97XRLk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 215
+        },
+        "outputId": "8254516b-13d0-4056-84f6-e8c6a3a5133b"
+      },
+      "source": [
+        "total.head()"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Product</th>\n",
+              "      <th>Product_Brand</th>\n",
+              "      <th>Item_Category</th>\n",
+              "      <th>Subcategory_1</th>\n",
+              "      <th>Subcategory_2</th>\n",
+              "      <th>Item_Rating</th>\n",
+              "      <th>Selling_Price</th>\n",
+              "      <th>month</th>\n",
+              "      <th>Day</th>\n",
+              "      <th>DayOfyear</th>\n",
+              "      <th>Week</th>\n",
+              "      <th>Quarter</th>\n",
+              "      <th>Is_month_start</th>\n",
+              "      <th>Is_month_end</th>\n",
+              "      <th>Unique_Item_category_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_1_per_product_brand</th>\n",
+              "      <th>Unique_Subcategory_2_per_product_brand</th>\n",
+              "      <th>std_rating_per_product_brand</th>\n",
+              "      <th>std_rating_Item_Category</th>\n",
+              "      <th>std_rating_Subcategory_1</th>\n",
+              "      <th>std_rating_Subcategory_2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>P-2610</td>\n",
+              "      <td>659</td>\n",
+              "      <td>9</td>\n",
+              "      <td>11</td>\n",
+              "      <td>159</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>5.676754</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>34</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.105474</td>\n",
+              "      <td>1.094790</td>\n",
+              "      <td>1.018614</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>P-2453</td>\n",
+              "      <td>3078</td>\n",
+              "      <td>17</td>\n",
+              "      <td>139</td>\n",
+              "      <td>387</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>6.800170</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>182</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "      <td>1.197104</td>\n",
+              "      <td>1.205383</td>\n",
+              "      <td>1.227087</td>\n",
+              "      <td>1.186726</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>P-6802</td>\n",
+              "      <td>1810</td>\n",
+              "      <td>38</td>\n",
+              "      <td>119</td>\n",
+              "      <td>118</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>6.675823</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.202082</td>\n",
+              "      <td>1.192523</td>\n",
+              "      <td>1.094301</td>\n",
+              "      <td>1.028398</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>P-4452</td>\n",
+              "      <td>3078</td>\n",
+              "      <td>12</td>\n",
+              "      <td>40</td>\n",
+              "      <td>155</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.731018</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "      <td>1.197104</td>\n",
+              "      <td>1.215595</td>\n",
+              "      <td>0.636396</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>P-8454</td>\n",
+              "      <td>3078</td>\n",
+              "      <td>17</td>\n",
+              "      <td>86</td>\n",
+              "      <td>344</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>6.154858</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>346</td>\n",
+              "      <td>50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>13</td>\n",
+              "      <td>29</td>\n",
+              "      <td>100</td>\n",
+              "      <td>1.197104</td>\n",
+              "      <td>1.205383</td>\n",
+              "      <td>1.146774</td>\n",
+              "      <td>1.123001</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Product  Product_Brand  ...  std_rating_Subcategory_1  std_rating_Subcategory_2\n",
+              "0  P-2610            659  ...                  1.094790                  1.018614\n",
+              "1  P-2453           3078  ...                  1.227087                  1.186726\n",
+              "2  P-6802           1810  ...                  1.094301                  1.028398\n",
+              "3  P-4452           3078  ...                  0.636396                       NaN\n",
+              "4  P-8454           3078  ...                  1.146774                  1.123001\n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QMBgaA6UXRLn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "9c811315-45c3-40be-e17e-c0ae0e899b4d"
+      },
+      "source": [
+        "train.shape"
+      ],
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(2452, 17)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LZ217hxZXRLq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "train_final=total[:train.shape[0]]\n",
+        "test_final=total[train.shape[0]:]"
+      ],
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "syjRNkLaXRLu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "1a72c04e-e3c5-46f8-d2bd-ba58e8ccf99e"
+      },
+      "source": [
+        "train_final.shape"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(2452, 21)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HJ4t2IfeXRLy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "2c5b065d-983d-4f68-f771-d8ca2c5ef0ff"
+      },
+      "source": [
+        "test_final.shape"
+      ],
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1051, 21)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "P1TF5FEQXRL1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X=train_final.drop(['Product','Selling_Price'],1)\n",
+        "y=train_final['Selling_Price']\n",
+        "test_final=test_final.drop(['Product','Selling_Price'],1)"
+      ],
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-q1tpJfoXRL4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "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=0.3,random_state=7)"
+      ],
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4XoXoLNmXRL9",
+        "colab_type": "text"
+      },
+      "source": [
+        "# XGBoost"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CetsqpekXRL9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "outputId": "50c94526-11c9-41a9-e5c9-ec716ba7344a"
+      },
+      "source": [
+        "from xgboost import XGBRegressor\n",
+        "model_2 = XGBRegressor(\n",
+        " learning_rate =0.01,\n",
+        " n_estimators=5000,\n",
+        " max_depth=6,\n",
+        " colsample_bytree=0.8,\n",
+        " seed=100,\n",
+        " eval_metric='rmse'\n",
+        " )\n",
+        "#model.fit(X_train, y_train)\n",
+        "model_2.fit(X_train, y_train, eval_metric='rmse', \n",
+        "          eval_set=[(X_test, y_test)], early_stopping_rounds=100, verbose=100)"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[12:20:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+            "[0]\tvalidation_0-rmse:6.18299\n",
+            "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
+            "[100]\tvalidation_0-rmse:2.39414\n",
+            "[200]\tvalidation_0-rmse:1.10503\n",
+            "[300]\tvalidation_0-rmse:0.748987\n",
+            "[400]\tvalidation_0-rmse:0.671509\n",
+            "[500]\tvalidation_0-rmse:0.655313\n",
+            "[600]\tvalidation_0-rmse:0.650131\n",
+            "[700]\tvalidation_0-rmse:0.647207\n",
+            "[800]\tvalidation_0-rmse:0.64594\n",
+            "[900]\tvalidation_0-rmse:0.643378\n",
+            "[1000]\tvalidation_0-rmse:0.640014\n",
+            "[1100]\tvalidation_0-rmse:0.638598\n",
+            "[1200]\tvalidation_0-rmse:0.637999\n",
+            "[1300]\tvalidation_0-rmse:0.637802\n",
+            "Stopping. Best iteration:\n",
+            "[1289]\tvalidation_0-rmse:0.637631\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
+              "             colsample_bynode=1, colsample_bytree=0.8, eval_metric='rmse',\n",
+              "             gamma=0, importance_type='gain', learning_rate=0.01,\n",
+              "             max_delta_step=0, max_depth=6, min_child_weight=1, missing=None,\n",
+              "             n_estimators=5000, n_jobs=1, nthread=None, objective='reg:linear',\n",
+              "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
+              "             seed=100, silent=None, subsample=1, verbosity=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eBGtXDSiXRMA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "4f8437fa-d3c0-47eb-8117-01a27b39df3b"
+      },
+      "source": [
+        "from xgboost import XGBRegressor\n",
+        "xgb=XGBRegressor(\n",
+        " learning_rate =0.01,\n",
+        " n_estimators=1350,\n",
+        " max_depth=6,\n",
+        " colsample_bytree=0.8,\n",
+        " seed=100)\n",
+        "xgb_model=xgb.fit(X,y)"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[12:21:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Nl8QRuRFXRMD",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101
+        },
+        "outputId": "07118b3d-13b3-40b4-8dba-a25890041693"
+      },
+      "source": [
+        "from sklearn.model_selection import cross_val_score\n",
+        "score=cross_val_score(X=X,y=y,estimator=xgb,scoring='neg_root_mean_squared_error',cv=5)"
+      ],
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[12:21:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+            "[12:21:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+            "[12:21:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+            "[12:21:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
+            "[12:22:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BzoUE6crXRMG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "a040ddc4-542f-414d-aa3e-da691dfcfcf2"
+      },
+      "source": [
+        "score"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-0.71462691, -0.64018146, -0.64697859, -0.64833916, -0.6607256 ])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "THxrWz4WXRMJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "ca2effd8-34f3-4693-dfba-2833c69ce4a4"
+      },
+      "source": [
+        "np.mean(score)"
+      ],
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-0.6621703441307158"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OxZJj9CKXRMM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "y_pred1=xgb.predict(test_final)"
+      ],
+      "execution_count": 46,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DXhIOJtkXRMQ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "y_pred1=np.expm1(y_pred1)"
+      ],
+      "execution_count": 47,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2QdZ1WECXRMV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "78f20947-5b80-4932-824c-2050cf895ac1"
+      },
+      "source": [
+        "y_pred1"
+      ],
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2244.199  ,  384.16357,  728.9483 , ..., 1351.9078 ,  440.58673,\n",
+              "       1341.4869 ], dtype=float32)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JMs8H-HUXRMY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 610
+        },
+        "outputId": "ba20c1ec-e7dc-4b67-d939-bdebc3e28866"
+      },
+      "source": [
+        "pd.DataFrame(list(zip(X.columns,xgb_model.feature_importances_*100)))"
+      ],
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>0</th>\n",
+              "      <th>1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Product_Brand</td>\n",
+              "      <td>1.207585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Item_Category</td>\n",
+              "      <td>3.546038</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Subcategory_1</td>\n",
+              "      <td>2.339068</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Subcategory_2</td>\n",
+              "      <td>7.091056</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Item_Rating</td>\n",
+              "      <td>0.702977</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>month</td>\n",
+              "      <td>2.606958</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Day</td>\n",
+              "      <td>2.642967</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>DayOfyear</td>\n",
+              "      <td>20.679478</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Week</td>\n",
+              "      <td>1.340544</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Quarter</td>\n",
+              "      <td>10.261303</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Is_month_start</td>\n",
+              "      <td>12.352847</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Is_month_end</td>\n",
+              "      <td>2.690032</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Unique_Item_category_per_product_brand</td>\n",
+              "      <td>1.673950</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Unique_Subcategory_1_per_product_brand</td>\n",
+              "      <td>3.244393</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Unique_Subcategory_2_per_product_brand</td>\n",
+              "      <td>1.369855</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>std_rating_per_product_brand</td>\n",
+              "      <td>1.600263</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>std_rating_Item_Category</td>\n",
+              "      <td>6.032631</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>std_rating_Subcategory_1</td>\n",
+              "      <td>15.682927</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>std_rating_Subcategory_2</td>\n",
+              "      <td>2.935128</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                         0          1\n",
+              "0                            Product_Brand   1.207585\n",
+              "1                            Item_Category   3.546038\n",
+              "2                            Subcategory_1   2.339068\n",
+              "3                            Subcategory_2   7.091056\n",
+              "4                              Item_Rating   0.702977\n",
+              "5                                    month   2.606958\n",
+              "6                                      Day   2.642967\n",
+              "7                                DayOfyear  20.679478\n",
+              "8                                     Week   1.340544\n",
+              "9                                  Quarter  10.261303\n",
+              "10                          Is_month_start  12.352847\n",
+              "11                            Is_month_end   2.690032\n",
+              "12  Unique_Item_category_per_product_brand   1.673950\n",
+              "13  Unique_Subcategory_1_per_product_brand   3.244393\n",
+              "14  Unique_Subcategory_2_per_product_brand   1.369855\n",
+              "15            std_rating_per_product_brand   1.600263\n",
+              "16                std_rating_Item_Category   6.032631\n",
+              "17                std_rating_Subcategory_1  15.682927\n",
+              "18                std_rating_Subcategory_2   2.935128"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AQI6tQuAXRMc",
+        "colab_type": "text"
+      },
+      "source": [
+        "# LGBM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6k3DS2ukXRMc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 252
+        },
+        "outputId": "135d97cd-2140-402b-d405-8dbd9d49306a"
+      },
+      "source": [
+        "from lightgbm import LGBMRegressor\n",
+        "lgb_fit_params={\"early_stopping_rounds\":50, \n",
+        "            \"eval_metric\" : 'rmse', \n",
+        "            \"eval_set\" : [(X_test,y_test)],\n",
+        "            'eval_names': ['valid'],\n",
+        "            'verbose':100\n",
+        "           }\n",
+        "\n",
+        "lgb_params = {'boosting_type': 'gbdt',\n",
+        " 'objective': 'regression',\n",
+        " 'metric': 'rmse',\n",
+        " 'verbose': 0,\n",
+        " 'bagging_fraction': 0.8,\n",
+        " 'bagging_freq': 1,\n",
+        " 'lambda_l1': 0.01,\n",
+        " 'lambda_l2': 0.01,\n",
+        " 'learning_rate': 0.01,\n",
+        " 'max_bin': 255,\n",
+        " 'max_depth': 6,\n",
+        " 'min_data_in_bin': 1,\n",
+        " 'min_data_in_leaf': 1,\n",
+        " 'num_leaves': 31}\n",
+        "lgb_params"
+      ],
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'bagging_fraction': 0.8,\n",
+              " 'bagging_freq': 1,\n",
+              " 'boosting_type': 'gbdt',\n",
+              " 'lambda_l1': 0.01,\n",
+              " 'lambda_l2': 0.01,\n",
+              " 'learning_rate': 0.01,\n",
+              " 'max_bin': 255,\n",
+              " 'max_depth': 6,\n",
+              " 'metric': 'rmse',\n",
+              " 'min_data_in_bin': 1,\n",
+              " 'min_data_in_leaf': 1,\n",
+              " 'num_leaves': 31,\n",
+              " 'objective': 'regression',\n",
+              " 'verbose': 0}"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QOvl-4j2XRMf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 218
+        },
+        "outputId": "11ae67b4-00e7-4cc6-e24f-9b91222fff28"
+      },
+      "source": [
+        "clf_lgb = LGBMRegressor(n_estimators=10000, **lgb_params, random_state=123456789, n_jobs=-1)\n",
+        "clf_lgb.fit(X_train, y_train, **lgb_fit_params)\n",
+        "clf_lgb.best_iteration_"
+      ],
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training until validation scores don't improve for 50 rounds.\n",
+            "[100]\tvalid's rmse: 0.771477\n",
+            "[200]\tvalid's rmse: 0.68349\n",
+            "[300]\tvalid's rmse: 0.656189\n",
+            "[400]\tvalid's rmse: 0.644552\n",
+            "[500]\tvalid's rmse: 0.639316\n",
+            "[600]\tvalid's rmse: 0.635627\n",
+            "[700]\tvalid's rmse: 0.633443\n",
+            "[800]\tvalid's rmse: 0.632627\n",
+            "Early stopping, best iteration is:\n",
+            "[832]\tvalid's rmse: 0.632087\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "832"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 51
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IiXoSWOkXRMj",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "clf_lgb=LGBMRegressor(n_estimators=int(clf_lgb.best_iteration_*1.2), **lgb_params)\n",
+        "lgb_model=clf_lgb.fit(X, y)"
+      ],
+      "execution_count": 52,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FO87-ydkXRMm",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from sklearn.model_selection import cross_val_score\n",
+        "score=cross_val_score(X=X,y=y,estimator=clf_lgb,scoring='neg_root_mean_squared_error',cv=5)"
+      ],
+      "execution_count": 53,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uTVwXienXRMp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "21f337dd-dbfb-4a7c-f25f-f239c1b04819"
+      },
+      "source": [
+        "score"
+      ],
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-0.70568776, -0.65095725, -0.64274763, -0.64769899, -0.66754616])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 54
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qnzauFdjXRMs",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "27496e8f-dd60-4aae-9655-fc7a90e12440"
+      },
+      "source": [
+        "np.mean(score)"
+      ],
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-0.6629275567395373"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fPRP77dSXRMv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 610
+        },
+        "outputId": "8d31be8a-8c0b-4ec9-e01d-8b5206e6986a"
+      },
+      "source": [
+        "pd.DataFrame(list(zip(X.columns,clf_lgb.feature_importances_)))"
+      ],
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>0</th>\n",
+              "      <th>1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Product_Brand</td>\n",
+              "      <td>4639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Item_Category</td>\n",
+              "      <td>1719</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Subcategory_1</td>\n",
+              "      <td>1664</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Subcategory_2</td>\n",
+              "      <td>2704</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Item_Rating</td>\n",
+              "      <td>3179</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>month</td>\n",
+              "      <td>825</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Day</td>\n",
+              "      <td>1523</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>DayOfyear</td>\n",
+              "      <td>1574</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Week</td>\n",
+              "      <td>1014</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Quarter</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Is_month_start</td>\n",
+              "      <td>35</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Is_month_end</td>\n",
+              "      <td>14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Unique_Item_category_per_product_brand</td>\n",
+              "      <td>140</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Unique_Subcategory_1_per_product_brand</td>\n",
+              "      <td>101</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Unique_Subcategory_2_per_product_brand</td>\n",
+              "      <td>136</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>std_rating_per_product_brand</td>\n",
+              "      <td>2721</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>std_rating_Item_Category</td>\n",
+              "      <td>1595</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>std_rating_Subcategory_1</td>\n",
+              "      <td>2248</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>std_rating_Subcategory_2</td>\n",
+              "      <td>3221</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                         0     1\n",
+              "0                            Product_Brand  4639\n",
+              "1                            Item_Category  1719\n",
+              "2                            Subcategory_1  1664\n",
+              "3                            Subcategory_2  2704\n",
+              "4                              Item_Rating  3179\n",
+              "5                                    month   825\n",
+              "6                                      Day  1523\n",
+              "7                                DayOfyear  1574\n",
+              "8                                     Week  1014\n",
+              "9                                  Quarter     7\n",
+              "10                          Is_month_start    35\n",
+              "11                            Is_month_end    14\n",
+              "12  Unique_Item_category_per_product_brand   140\n",
+              "13  Unique_Subcategory_1_per_product_brand   101\n",
+              "14  Unique_Subcategory_2_per_product_brand   136\n",
+              "15            std_rating_per_product_brand  2721\n",
+              "16                std_rating_Item_Category  1595\n",
+              "17                std_rating_Subcategory_1  2248\n",
+              "18                std_rating_Subcategory_2  3221"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PitGQCpxXRMx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "y_pred2=lgb_model.predict(test_final)"
+      ],
+      "execution_count": 57,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SHbdGDozXRM0",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "y_pred2=np.expm1(y_pred2)"
+      ],
+      "execution_count": 58,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lA4l7CpVXRM5",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "80df3d49-a109-4e00-b709-cdd300aa0df7"
+      },
+      "source": [
+        "y_pred2"
+      ],
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2227.90984163,  383.77716397,  744.16048133, ..., 1356.15990238,\n",
+              "        397.34376471, 1349.76831087])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 59
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SwZtucoAXRM_",
+        "colab_type": "text"
+      },
+      "source": [
+        "# CatBoost Regressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9Qa8zmdzY6DP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 339
+        },
+        "outputId": "99ec8e4a-f0cd-4d9f-910a-d496ec4e2111"
+      },
+      "source": [
+        "! pip install catboost\n"
+      ],
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Collecting catboost\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/aa/e61819d04ef2bbee778bf4b3a748db1f3ad23512377e43ecfdc3211437a0/catboost-0.23.2-cp36-none-manylinux1_x86_64.whl (64.8MB)\n",
+            "\u001b[K     |████████████████████████████████| 64.8MB 62kB/s \n",
+            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
+            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.0.4)\n",
+            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.5)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.1)\n",
+            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
+            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
+            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2.8.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
+            "Installing collected packages: catboost\n",
+            "Successfully installed catboost-0.23.2\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Z3Kj-y6FXRM_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "a40f8121-82c6-4fed-f7f3-fba4606dcbeb"
+      },
+      "source": [
+        "from catboost import CatBoostRegressor\n",
+        "cbr=CatBoostRegressor()\n",
+        "cbr.fit(X,y)"
+      ],
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Learning rate set to 0.045786\n",
+            "0:\tlearn: 1.1495506\ttotal: 50.2ms\tremaining: 50.1s\n",
+            "1:\tlearn: 1.1237286\ttotal: 52.8ms\tremaining: 26.4s\n",
+            "2:\tlearn: 1.1007454\ttotal: 55.3ms\tremaining: 18.4s\n",
+            "3:\tlearn: 1.0778302\ttotal: 57.8ms\tremaining: 14.4s\n",
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+            "9:\tlearn: 0.9657049\ttotal: 73.8ms\tremaining: 7.3s\n",
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+            "999:\tlearn: 0.3679254\ttotal: 2.8s\tremaining: 0us\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<catboost.core.CatBoostRegressor at 0x7f3c2dcf4cc0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "H-TueJ__XRND",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 63,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wKb8XzVtXRNH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "ddcb793e-a1db-4511-d70b-a17c7d98363a"
+      },
+      "source": [
+        "from sklearn.model_selection import cross_val_score\n",
+        "score=cross_val_score(X=X,y=y,estimator=cbr,scoring='neg_root_mean_squared_error',cv=5)"
+      ],
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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+            "994:\tlearn: 0.3471566\ttotal: 2.55s\tremaining: 12.8ms\n",
+            "995:\tlearn: 0.3471111\ttotal: 2.55s\tremaining: 10.2ms\n",
+            "996:\tlearn: 0.3468797\ttotal: 2.55s\tremaining: 7.68ms\n",
+            "997:\tlearn: 0.3468131\ttotal: 2.56s\tremaining: 5.12ms\n",
+            "998:\tlearn: 0.3466159\ttotal: 2.56s\tremaining: 2.56ms\n",
+            "999:\tlearn: 0.3464517\ttotal: 2.56s\tremaining: 0us\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-v7PBu4SXRNM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "92795301-22ea-40ba-ea3c-4ec68783d11b"
+      },
+      "source": [
+        "score"
+      ],
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-0.71938924, -0.61967382, -0.63893084, -0.65928774, -0.65824295])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vGJqnUeyXRNO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "2ca850dc-80a7-45e4-9837-ec1bbcd20be8"
+      },
+      "source": [
+        "np.mean(score)"
+      ],
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-0.659104919366701"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YW6JR8lFXRNR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "74438d8b-73a0-4c8a-b85c-4c9f66d9ad08"
+      },
+      "source": [
+        "y_pred3=cbr.predict(test_final)\n",
+        "\n",
+        "y_pred3=np.expm1(y_pred3)\n",
+        "\n",
+        "y_pred3"
+      ],
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2164.94455524,  377.77405189,  823.4295862 , ..., 1360.93694143,\n",
+              "        576.30806807, 1344.81052795])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eDrx6k1YXRNU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "e6c18eb6-3516-4acf-9f6c-1b12dea5f0d6"
+      },
+      "source": [
+        "y_pred=(0.1*y_pred1)+(y_pred2*0.8)+(y_pred3*0.1)\n",
+        "y_pred"
+      ],
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2223.24223545,  383.21549531,  750.56617248, ..., 1356.21239669,\n",
+              "        419.56449243, 1348.44439839])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "T7cF9NUOXRNW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "submission_new=pd.DataFrame(np.round(abs(y_pred),2),columns=['Selling_Price'])"
+      ],
+      "execution_count": 69,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2YyiH4ebXRNZ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "bc850989-3088-4575-9b44-48ae3182a0b8"
+      },
+      "source": [
+        "submission_new.head()"
+      ],
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Selling_Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2223.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>383.22</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>750.57</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>358.55</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>741.44</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Selling_Price\n",
+              "0        2223.24\n",
+              "1         383.22\n",
+              "2         750.57\n",
+              "3         358.55\n",
+              "4         741.44"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 70
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EFvZ3YpNXRNb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "submission_new.to_excel('submission_final.xlsx',index=False)"
+      ],
+      "execution_count": 71,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IGKqQi1KXRNe",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
diff --git a/Week_8/readme.md b/Week_8/readme.md
new file mode 100644
index 0000000..17a2093
--- /dev/null
+++ b/Week_8/readme.md
@@ -0,0 +1,6 @@
+Week 8 - E-COMMERCE PRICE PREDICTION’ HACKATHON" 
+
+Top 3 approach Link 
+
+https://analyticsindiamag.com/meet-the-machinehack-champions-who-cracked-the-e-commerce-price-prediction-hackathon/
+