assignment/Assignement2(cls).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb6778c1",
   "metadata": {},
   "source": [
    "# Problem Statment:- \n",
    "- We have data of wholesale store and in that data we have to apply classification algorithm and grouped the same data into one group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "572f52b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e4b094b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing data\n",
    "data = pd.read_csv('Wholesale customers data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf8d0362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f54d11",
   "metadata": {},
   "source": [
    "# Observation:\n",
    "- There are 440 rows and 8 columns in dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0491f7cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel               2\n",
       "Region                3\n",
       "Fresh               433\n",
       "Milk                421\n",
       "Grocery             430\n",
       "Frozen              426\n",
       "Detergents_Paper    417\n",
       "Delicassen          403\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "961c583c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel             0\n",
       "Region              0\n",
       "Fresh               0\n",
       "Milk                0\n",
       "Grocery             0\n",
       "Frozen              0\n",
       "Detergents_Paper    0\n",
       "Delicassen          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f317f2b",
   "metadata": {},
   "source": [
    "No null value in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0bab05b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cba8a166",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7ccc4b",
   "metadata": {},
   "source": [
    "# Outliers Treatment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca61bb5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name = ['Fresh','Milk','Grocery','Frozen','Detergents_Paper','Delicassen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c1d3f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[col_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ad07bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8504.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_=data.Fresh.median()\n",
    "median_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c75b8a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of outliers in Fresh \n",
    "data[data['Fresh']>data['Fresh'].quantile(0.75)].Fresh.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e393726d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing outliers form Fresh column\n",
    "data['Fresh']=np.where(data['Fresh']>data['Fresh'].quantile(0.80),median_,data['Fresh'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4bda6a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Fresh'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd1a27b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3627.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for Milk column outlier treatment\n",
    "median_ = data['Milk'].median()\n",
    "median_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5c4db37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting the outliers in the Milk column\n",
    "data[data['Milk']>data['Milk'].quantile(0.75)]['Milk'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12ccf559",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the outliers with the median\n",
    "data['Milk'] = np.where(data['Milk']>data['Milk'].quantile(0.80),median_,data['Milk'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "af9715f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Milk'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70ab411b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4755.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Grocery column outliers treatment\n",
    "median_ = data['Grocery'].median()\n",
    "q3 = data['Grocery'].quantile(0.75)\n",
    "q1 = data['Grocery'].quantile(0.25)\n",
    "iqr = q3-q1\n",
    "uw = q3+1.5*iqr\n",
    "median_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25348ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting the no. of outliers in the Grocery column\n",
    "data[data['Grocery']>uw]['Grocery'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "60fd5c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Grocery'] = np.where(data['Grocery']>uw,median_,data['Grocery'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1381d9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAO9klEQVR4nO3dXYjVd37H8ffXGdNOWpb4FAmjqWlH2NiFptnBBNoLWx9ivDELJSQXdQhhDSQxFnpRtzeWpFvSi7Yksg1YVjJCuza0XSLF1U5kyVIwiZM2xDzWg43oYHR2Jo2C203GfHsxv2mP03k4M3Oc/xl8v+Bw/r/v/+F8j6if8386JzITSdLNbVHVDUiSqmcYSJIMA0mSYSBJwjCQJAHtVTcwW8uXL881a9ZU3YYkLShvv/32TzNzxfj6gg2DNWvW0N/fX3UbkrSgRMTZieoeJpIkGQaSJMNAkoRhIEnCMJCaamhoiGeeeYahoaGqW5FmxDCQmqi3t5dTp05x8ODBqluRZsQwkJpkaGiIo0ePkpkcPXrUvQMtKIaB1CS9vb189dVXAFy7ds29Ay0ohoHUJK+99hojIyMAjIyM0NfXV3FHUuMMA6lJNm3aRHv76E397e3tbN68ueKOpMYZBlKT9PT0sGjR6D+ptrY2duzYUXFHUuMMA6lJli1bxoYNGwDYsGEDy5Ytq7YhaQYMA6mJrly5AsDly5cr7kSaGcNAapKhoSFOnDgBwIkTJ7y0VAuKYSA1yb59+6YcS63MMJCa5PXXX59yLLUyw0Bqksycciy1MsNAapK2trYpx1IrMwykJrl27dqUY6mVGQZSk6xZs2bKsdTKDAOpScbfcdzT01NRJ9LMGQZSk4z/ltLe3t6KOpFmzjCQmuSTTz6Zciy1MsNAapJVq1ZNOZZamWEgNcnq1auvG995550VdSLNnGEgNcnJkyevG7/11lsVdSLNnGEgNYl3IGshMwykJtm4ceN1402bNlXUiTRzhoHUJE888cR14507d1bUiTRzhoEkyTCQmsXfM9BCZhhITeLvGWghmzYMImJ1RPw4Ij6IiPcjYnepL42Ivog4XZ6XlHpExIsRUYuIdyPi3rpt9ZTlT0dET139mxFxqqzzYkTEjXiz0o3k1URayBrZMxgB/jAz1wH3A09FxDpgD3A8M9cCx8sY4EFgbXnsBF6C0fAA9gL3AeuBvWMBUpb5dt16W+f+1qT5deutt045llrZtGGQmRcy89/K9BXgQ6AT2A6MfRNXL/BQmd4OHMxRbwC3RcQdwANAX2YOZ+ZnQB+wtcz7Wma+kaMfpQ7WbUtaMEZGRqYcS61sRucMImIN8JvAm8DKzLxQZn0KrCzTncC5utXOl9pU9fMT1Cd6/Z0R0R8R/YODgzNpXbrh2tvbpxxLrazhMIiIXwb+EfiDzLxcP698or/hB0gzc39mdmdm94oVK270y0kzcvXq1SnHUitrKAwiYjGjQfC3mflPpXyxHOKhPF8q9QGg/hu7VpXaVPVVE9QlSfOkkauJAvg+8GFm/mXdrMPA2BVBPcCrdfUd5aqi+4HPy+GkY8CWiFhSThxvAY6VeZcj4v7yWjvqtiUtGLfffvt145UrV06ypNR6Gjmo+VvA7wOnIuKdUvtj4HnglYh4HDgLPFzmHQG2ATXgKvAYQGYOR8RzwNhXOz6bmcNl+kngZaAD+FF5SAvK3XffzaVLl64bSwvFtGGQmf8KTHbd/8bxhXL+4KlJtnUAODBBvR/4xnS9SK1s/FdWv/nmmxV1Is2cdyBLTTL+sJCHibSQGAZSk1y8eHHKsdTKDAOpSTZv3szYN6lEBFu2bKm4I6lx3hWjOdu3bx+1Wq3qNir35ZdfXvd9RKdPn2b37t0VdlS9rq4udu3aVXUbaoB7BlKTLF68+H/vOl66dCmLFy+uuCOpce4ZaM785Pd/nnzySc6ePcv+/ftZtmxZ1e1IDXPPQGqixYsX09XVZRBowTEMJEmGgSTJMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAk0UAYRMSBiLgUEe/V1f4kIgYi4p3y2FY37zsRUYuIjyPigbr61lKrRcSeuvpdEfFmqf99RNzSzDcoSZpeI3sGLwNbJ6j/VWbeUx5HACJiHfAI8Otlnb+OiLaIaAO+BzwIrAMeLcsC/HnZVhfwGfD4XN6QJGnmpg2DzPwJMNzg9rYDhzLz55n5n0ANWF8etcw8k5lfAIeA7RERwO8C/1DW7wUemtlbkCTN1VzOGTwdEe+Ww0hLSq0TOFe3zPlSm6y+DPivzBwZV59QROyMiP6I6B8cHJxD65KkerMNg5eAXwPuAS4Af9GshqaSmfszszszu1esWDEfLylJN4X22ayUmRfHpiPib4B/LsMBYHXdoqtKjUnqQ8BtEdFe9g7ql5ckzZNZ7RlExB11w28BY1caHQYeiYhfiIi7gLXAW8BJYG25cugWRk8yH87MBH4M/F5Zvwd4dTY9SZJmb9o9g4j4AbABWB4R54G9wIaIuAdI4BPgCYDMfD8iXgE+AEaApzLzWtnO08AxoA04kJnvl5f4I+BQRPwp8O/A95v15iRJjZk2DDLz0QnKk/6HnZnfBb47Qf0IcGSC+hlGrzaSJFXEO5AlSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkkQDYRARByLiUkS8V1dbGhF9EXG6PC8p9YiIFyOiFhHvRsS9dev0lOVPR0RPXf2bEXGqrPNiRESz36QkaWqN7Bm8DGwdV9sDHM/MtcDxMgZ4EFhbHjuBl2A0PIC9wH3AemDvWICUZb5dt97415Ik3WDThkFm/gQYHlfeDvSW6V7gobr6wRz1BnBbRNwBPAD0ZeZwZn4G9AFby7yvZeYbmZnAwbptSZLmyWzPGazMzAtl+lNgZZnuBM7VLXe+1Kaqn5+gPqGI2BkR/RHRPzg4OMvWJUnjzfkEcvlEn03opZHX2p+Z3ZnZvWLFivl4SUm6Kcw2DC6WQzyU50ulPgCsrltuValNVV81QV2SNI9mGwaHgbErgnqAV+vqO8pVRfcDn5fDSceALRGxpJw43gIcK/MuR8T95SqiHXXbkiTNk/bpFoiIHwAbgOURcZ7Rq4KeB16JiMeBs8DDZfEjwDagBlwFHgPIzOGIeA44WZZ7NjPHTko/yegVSx3Aj8pDkjSPpg2DzHx0klkbJ1g2gacm2c4B4MAE9X7gG9P1IUm6cbwDWZJkGEiSDANJEg2cM9DE9u3bR61Wq7oNtZixvxO7d++uuBO1mq6uLnbt2lV1G5MyDGapVqvxznsfcu3WpVW3ohay6IvR+y/fPnOx4k7UStqujv9Gn9ZjGMzBtVuX8rOvb6u6DUktruOjI1W3MC3PGUiSDANJkmEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCQxxzCIiE8i4lREvBMR/aW2NCL6IuJ0eV5S6hERL0ZELSLejYh767bTU5Y/HRE9c3tLkqSZasaewe9k5j2Z2V3Ge4DjmbkWOF7GAA8Ca8tjJ/ASjIYHsBe4D1gP7B0LEEnS/LgRh4m2A71luhd4qK5+MEe9AdwWEXcADwB9mTmcmZ8BfcDWG9CXJGkScw2DBP4lIt6OiJ2ltjIzL5TpT4GVZboTOFe37vlSm6z+/0TEzojoj4j+wcHBObYuSRrTPsf1fzszByLidqAvIj6qn5mZGRE5x9eo395+YD9Ad3d307Y7GwMDA7Rd/ZyOj45U2YakBaDt6hADAyNVtzGlOe0ZZOZAeb4E/JDRY/4Xy+EfyvOlsvgAsLpu9VWlNlldkjRPZr1nEBG/BCzKzCtlegvwLHAY6AGeL8+vllUOA09HxCFGTxZ/npkXIuIY8Gd1J423AN+ZbV/zpbOzk09/3s7Pvr6t6lYktbiOj47Q2bly+gUrNJfDRCuBH0bE2Hb+LjOPRsRJ4JWIeBw4Czxclj8CbANqwFXgMYDMHI6I54CTZblnM3N4Dn1JkmZo1mGQmWeA35igPgRsnKCewFOTbOsAcGC2vUiS5sY7kCVJhoEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIElibr+BfNNruzpMx0dHqm5DLWTRf18G4Ktf/FrFnaiVtF0dZvRn41uXYTBLXV1dVbegFlSrXQGg61db+x++5tvKlv8/wzCYpV27dlXdglrQ7t27AXjhhRcq7kSaGc8ZSJIMA0mSYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSaKEwiIitEfFxRNQiYk/V/UjSzaQlwiAi2oDvAQ8C64BHI2JdtV1J0s2jVX7PYD1Qy8wzABFxCNgOfFBpV2rIvn37qNVqVbfREsb+HMZ+1+Bm19XV5W9/LBAtsWcAdALn6sbnS+06EbEzIvojon9wcHDempMa1dHRQUdHR9VtSDPWKnsGDcnM/cB+gO7u7qy4HRV+8pMWvlbZMxgAVteNV5WaJGketEoYnATWRsRdEXEL8AhwuOKeJOmm0RKHiTJzJCKeBo4BbcCBzHy/4rYk6abREmEAkJlHgCNV9yFJN6NWOUwkSaqQYSBJMgwkSYaBJAmIzIV571ZEDAJnq+5DmsBy4KdVNyFN4lcyc8X44oINA6lVRUR/ZnZX3Yc0Ex4mkiQZBpIkw0C6EfZX3YA0U54zkCS5ZyBJMgwkSRgGUlNFxNaI+DgiahGxp+p+pEZ5zkBqkohoA/4D2MzoT7eeBB7NTH/LWy3PPQOpedYDtcw8k5lfAIeA7RX3JDXEMJCapxM4Vzc+X2pSyzMMJEmGgdREA8DquvGqUpNanmEgNc9JYG1E3BURtwCPAIcr7klqSMv8BrK00GXmSEQ8DRwD2oADmfl+xW1JDfHSUkmSh4kkSYaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJwP8AQYnv066Rea4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Grocery'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "810e69e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2769438771365644"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Grocery'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d89c2c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1526.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Frozen column\n",
    "median_ = data['Frozen'].median()\n",
    "median_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97f0e901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting the no. of outliers in the data['Frozen']\n",
    "data[data['Frozen']>data['Frozen'].quantile(0.80)]['Frozen'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "207a2b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing outliers from the data\n",
    "data['Frozen']=np.where(data['Frozen']>data['Frozen'].quantile(0.80),median_,data['Frozen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f72e2c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4Qa4k6ewx9CWpIoa+JFUkMrPtGpYUEdPAy23XIS1hPfDztouQFvF7mbnoF536OvSlfhYRBzNza9t1SE14ekeSKmLoS1JFDH3pzN3VdgFSU57Tl6SKONOXpIoY+pJUEUNfasj7PGuQeU5faqDc5/m/mHOfZ+Dz3udZg8KZvtSM93nWQDP0pWYWu8/zaEu1SI0Z+pJUEUNfasb7PGugGfpSM97nWQOt57dLlAaZ93nWoHPJpiRVxNM7klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKGviRV5P8AKW3FvCmnveQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Frozen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1f82041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8930266482792876"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Frozen'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c1e11c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Detergents_Paper     3.631851\n",
       "Delicassen          11.151586\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Detergents_Paper','Delicassen']].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4f82cf7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "816.5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outlier treatment for column Detergents_Paper\n",
    "median_ = data['Detergents_Paper'].median()\n",
    "median_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b42acd91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting outliers in the data\n",
    "data[data['Detergents_Paper']>data['Detergents_Paper'].quantile(0.80)]['Detergents_Paper'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4cf1ef47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outliers treatment\n",
    "data['Detergents_Paper']=np.where(data['Detergents_Paper']>data['Detergents_Paper'].quantile(0.80),median_,data['Detergents_Paper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cdddfaaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7179795278475278"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Detergents_Paper'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d567a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "965.5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for Delicassen column outliers treatment\n",
    "median_ = data['Delicassen'].median()\n",
    "median_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d29d84fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting no. of Outliers in Delicassen column\n",
    "data[data['Delicassen']>data['Delicassen'].quantile(0.80)]['Delicassen'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4fbe0e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlier treatment\n",
    "data['Delicassen'] = np.where(data['Delicassen']>data['Delicassen'].quantile(0.80),median_,data['Delicassen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b27a14bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3643420440262236"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Delicassen'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2697edbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6f6badd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying StandardScaler to the columns \n",
    "data[col_name]=ss.fit_transform(data[col_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2e792d20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.106602</td>\n",
       "      <td>0.179574</td>\n",
       "      <td>0.251637</td>\n",
       "      <td>-1.254303</td>\n",
       "      <td>1.423302</td>\n",
       "      <td>0.975853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.067612</td>\n",
       "      <td>0.179574</td>\n",
       "      <td>0.627928</td>\n",
       "      <td>0.302990</td>\n",
       "      <td>1.961235</td>\n",
       "      <td>1.839085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.214912</td>\n",
       "      <td>0.179574</td>\n",
       "      <td>0.274698</td>\n",
       "      <td>0.949850</td>\n",
       "      <td>2.155029</td>\n",
       "      <td>0.241711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.231305</td>\n",
       "      <td>-1.052275</td>\n",
       "      <td>-0.374579</td>\n",
       "      <td>0.065573</td>\n",
       "      <td>-0.459895</td>\n",
       "      <td>1.862735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.235148</td>\n",
       "      <td>1.083066</td>\n",
       "      <td>0.183578</td>\n",
       "      <td>2.468915</td>\n",
       "      <td>0.643779</td>\n",
       "      <td>0.241711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region     Fresh      Milk   Grocery    Frozen  Detergents_Paper  \\\n",
       "0        2       3  1.106602  0.179574  0.251637 -1.254303          1.423302   \n",
       "1        2       3 -0.067612  0.179574  0.627928  0.302990          1.961235   \n",
       "2        2       3 -0.214912  0.179574  0.274698  0.949850          2.155029   \n",
       "3        1       3  1.231305 -1.052275 -0.374579  0.065573         -0.459895   \n",
       "4        2       3  0.235148  1.083066  0.183578  2.468915          0.643779   \n",
       "\n",
       "   Delicassen  \n",
       "0    0.975853  \n",
       "1    1.839085  \n",
       "2    0.241711  \n",
       "3    1.862735  \n",
       "4    0.241711  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9a87c295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Channel.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c8ed0c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Region.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd7ae20",
   "metadata": {},
   "source": [
    "# Applying KMean clustering algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "92adf088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "km = KMeans()\n",
    "km"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4283e8e0",
   "metadata": {},
   "source": [
    "# Spearating x "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "80f8f161",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ff8bf266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.        ,  3.        ,  1.10660195, ..., -1.25430309,\n",
       "         1.42330242,  0.97585284],\n",
       "       [ 2.        ,  3.        , -0.06761213, ...,  0.30298987,\n",
       "         1.96123466,  1.83908506],\n",
       "       [ 2.        ,  3.        , -0.21491197, ...,  0.94984993,\n",
       "         2.15502931,  0.24171129],\n",
       "       ...,\n",
       "       [ 2.        ,  3.        ,  1.49619329, ..., -1.02996437,\n",
       "        -0.19092881,  2.01843239],\n",
       "       [ 1.        ,  3.        ,  0.60883728, ..., -0.42535645,\n",
       "        -0.75449756,  2.52691164],\n",
       "       [ 1.        ,  3.        , -0.96103588, ..., -1.40419756,\n",
       "        -0.48596596, -1.55866001]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0c0374",
   "metadata": {},
   "source": [
    "# Applying Kmean to x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "316c074d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5d550b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.21538462,  2.93846154, -0.56033268,  0.48657976, -0.33253615,\n",
       "        -0.30921724, -0.26033123,  0.31787706],\n",
       "       [ 1.05263158,  2.89473684, -0.0891163 , -0.91022549, -0.71251112,\n",
       "        -0.45200229, -0.49263809, -0.95364801],\n",
       "       [ 1.8245614 ,  2.45614035, -0.72975663,  0.61398025,  1.86871396,\n",
       "        -0.4574956 , -0.21799847,  0.05200835],\n",
       "       [ 1.72916667,  2.6875    , -0.44357719,  0.7758318 ,  0.60147245,\n",
       "        -0.3738407 ,  2.37500362,  0.05482956],\n",
       "       [ 1.14583333,  1.02083333, -0.09251954, -0.21123772, -0.53533125,\n",
       "        -0.10683068, -0.35092165, -0.34408275],\n",
       "       [ 1.0862069 ,  2.62068966,  0.86505719, -0.57446814, -0.65625567,\n",
       "         0.09015082, -0.59932999,  0.93341161],\n",
       "       [ 1.04761905,  2.45238095,  0.31528376, -0.83791837, -0.61306519,\n",
       "         1.9238287 , -0.44373503, -0.2787573 ],\n",
       "       [ 1.60869565,  2.93478261,  1.02408693,  0.95573449,  0.64969105,\n",
       "         0.381983  ,  0.50065247,  0.44140979]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Cluster center\n",
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ceb2ec",
   "metadata": {},
   "source": [
    "# Spliting x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "170945c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('Channel',axis=1)\n",
    "y = data['Channel']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbb915b",
   "metadata": {},
   "source": [
    "# Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fe6b1401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569c6712",
   "metadata": {},
   "source": [
    "# Applying Cross validataion with KFold 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8b07d16c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86363636, 0.86363636, 0.79545455, 0.75      , 0.81818182,\n",
       "       0.84090909, 0.84090909, 0.86363636, 0.86363636, 0.77272727])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(lr,x,y,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0334e665",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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