laptop_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"laptop_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns =['Unnamed: 0'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] =df['Ram'].str.replace('GB', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Weight']=df['Weight'].str.replace('kg', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')\n",
    "df['Inches'] = df['Inches'].astype('float32')\n",
    "df['Price'] = df['Price'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float32\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float32\n",
      "dtypes: float32(3), int32(1), object(7)\n",
      "memory usage: 91.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\834922981.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['Price'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Ram', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x= df['Ram'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Weight', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Weight'],bins=20, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IJGBSRURERCQBkyoiIiIiCZhUEREREUnApIqIiIhIAiZVRERERBIwqSIiIiKSgEkVERERkQRMqoiIiIgkYFJFREREJAGTKiIiIiIJmFQRERERScCkioiIiEgCJlVEREREEjCpIiIiIpKASRURERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISAImVUREREQS6DSpOn78ODp16gQ7OzuoVCoEBQWp3R80aBBUKpXao23btmplHj58iH79+sHCwgJWVlbw8fFBcnKyWplLly7h448/hpGREezt7bFw4cIcbdmxYwdq1KgBIyMjuLi44MCBA2r3hRCYPn06ypcvD2NjY3h4eODWrVtyvhFERERU5Ok0qUpJSUHdunWxYsWK15Zp27Yt7t+/rzy2bt2qdr9fv36IiopCcHAw9u3bh+PHj2P48OHK/aSkJHh6esLBwQERERH49ttvMXPmTKxevVopExYWhj59+sDHxwcXLlxA165d0bVrV1y5ckUps3DhQixduhSrVq3C6dOnYWpqCi8vLzx79kzid4SIiIiKqhK6DN6uXTu0a9fujWUMDQ1ha2ub671r167h4MGDOHv2LBo1agQAWLZsGdq3b4/vvvsOdnZ22Lx5M9LS0rBu3ToYGBigVq1aiIyMxKJFi5Tka8mSJWjbti38/f0BAHPmzEFwcDCWL1+OVatWQQiBxYsXY+rUqejSpQsAYOPGjbCxsUFQUBB69+4t61tCRERERVShn1N17NgxlCtXDtWrV8fIkSPx33//KffCw8NhZWWlJFQA4OHhAT09PZw+fVop06xZMxgYGChlvLy8cOPGDTx69Egp4+HhoRbXy8sL4eHhAICYmBjExcWplbG0tISbm5tSJjfPnz9HUlKS2oOIiIiKp0KdVLVt2xYbN25ESEgIFixYgNDQULRr1w4ZGRkAgLi4OJQrV07ta0qUKAFra2vExcUpZWxsbNTKZL3Oq0z2+9m/LrcyuZk3bx4sLS2Vh729/Tv9+4mIiKjo0OnwX16yD6u5uLigTp06qFy5Mo4dO4bWrVvrsGVvZ8qUKfDz81NeJyUlMbEiIiIqpgp1T9WrnJycUKZMGURHRwMAbG1t8eDBA7Uy6enpePjwoTIPy9bWFvHx8Wplsl7nVSb7/exfl1uZ3BgaGsLCwkLtQURERMVTkUqq/vrrL/z3338oX748AMDd3R2JiYmIiIhQyhw5cgSZmZlwc3NTyhw/fhwvXrxQygQHB6N69eooVaqUUiYkJEQtVnBwMNzd3QEAjo6OsLW1VSuTlJSE06dPK2WIiIjo/abTpCo5ORmRkZGIjIwE8HJCeGRkJGJjY5GcnAx/f3+cOnUKd+/eRUhICLp06YIqVarAy8sLAODs7Iy2bdti2LBhOHPmDE6ePIlRo0ahd+/esLOzAwD07dsXBgYG8PHxQVRUFAIDA7FkyRK1YbmxY8fi4MGD+P7773H9+nXMnDkT586dw6hRowAAKpUK48aNw9dff41ff/0Vly9fxoABA2BnZ4euXbsW6PeMiIiICiedzqk6d+4cWrZsqbzOSnQGDhyIlStX4tKlS9iwYQMSExNhZ2cHT09PzJkzB4aGhsrXbN68GaNGjULr1q2hp6cHb29vLF26VLlvaWmJw4cPw9fXFw0bNkSZMmUwffp0tb2sPvzwQ2zZsgVTp07Fl19+iapVqyIoKAi1a9dWykycOBEpKSkYPnw4EhMT8dFHH+HgwYMwMjLS5reIiIiIigiVEELouhHvi6SkJFhaWuLx48ecX0VEJMk3e+6/8X7YhnF49uRfAICReRl8OHBxnnV+1a28jKZRMfG2n99Fak4VERERUWHFpIqIiIhIAiZVRERERBIwqSIiIiKSgEkVERERkQRMqoiIiIgk0CipSktLw40bN5Ceni6rPURERERFUr6SqtTUVPj4+MDExAS1atVCbGwsAGD06NGYP3++1AYSERERFQX5SqqmTJmCixcv4tixY2o7int4eCAwMFBa44iIiIiKinwdUxMUFITAwEA0adIEKpVKuV6rVi3cvn1bWuOIiIiIiop89VQlJCSgXLlyOa6npKSoJVlERERE74t8JVWNGjXC/v37lddZidSaNWvg7u4up2VERERERUi+hv/mzp2Ldu3a4erVq0hPT8eSJUtw9epVhIWFITQ0VHYbiYiIiAq9fPVUffTRR4iMjER6ejpcXFxw+PBhlCtXDuHh4WjYsKHsNhIREREVevnqqQKAypUr46effpLZFiIiIqIiK189VQcOHMChQ4dyXD906BB+++03jRtFREREVNTkK6maPHkyMjIyclwXQmDy5MkaN4qIiIioqMlXUnXr1i3UrFkzx/UaNWogOjpa40YRERERFTX5SqosLS1x586dHNejo6NhamqqcaOIiIiIipp8JVVdunTBuHHj1HZPj46OxoQJE9C5c2dpjSMiIiIqKvKVVC1cuBCmpqaoUaMGHB0d4ejoCGdnZ5QuXRrfffed7DYSERERFXr52lLB0tISYWFhCA4OxsWLF2FsbIw6deqgWbNmsttHREREVCTke58qlUoFT09PeHp6ymwPERERUZH01knV0qVLMXz4cBgZGWHp0qVvLDtmzBiNG0ZERERUlLx1UvXDDz+gX79+MDIywg8//PDaciqVikkVERERvXfeOqmKiYnJ9TkRERER5WP134sXL1C5cmVcu3ZNG+0hIiIiKpLeOakqWbIknj17po22EBERERVZ+dqnytfXFwsWLEB6errs9hAREREVSfnaUuHs2bMICQnB4cOH4eLikuNomt27d0tpHBEREVFRka+kysrKCt7e3rLbQkRERKQ1Y8eORUJCAgCgbNmyWLJkidT63ympyszMxLfffoubN28iLS0NrVq1wsyZM2FsbCy1UURERESyJSQkID4+Xmv1v9Ocqm+++QZffvklzMzMUKFCBSxduhS+vr7aahsRERFRkfFOSdXGjRvx448/4tChQwgKCsLevXuxefNmZGZmaqt9REREREXCOyVVsbGxaN++vfLaw8MDKpUK//zzj/SGERERERUl75RUpaenw8jISO1ayZIl8eLFC6mNIiIiIipq3mmiuhACgwYNgqGhoXLt2bNnGDFihNq2CtxSgYiIiN4375RUDRw4MMe1Tz/9VFpjiIiIqPDQ9hYExc07JVXr16/XVjuIiIiokNH2FgTFTb6OqSEiIiIidUyqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISIJ32lKBiIioqDE0s871OZFsTKqIqNjixoUEAA29p+u6CfSeYFJFRMUWNy4ken88WBacZ5mMpKdqz/P6mnKj27xTGzinioiIiEgCJlVEREREEjCpIiIiIpKASRURERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISAImVUREREQSMKkiIiIikkCnSdXx48fRqVMn2NnZQaVSISgoSO2+EALTp09H+fLlYWxsDA8PD9y6dUutzMOHD9GvXz9YWFjAysoKPj4+SE5OVitz6dIlfPzxxzAyMoK9vT0WLlyYoy07duxAjRo1YGRkBBcXFxw4cOCd20JERETvL50mVSkpKahbty5WrFiR6/2FCxdi6dKlWLVqFU6fPg1TU1N4eXnh2bNnSpl+/fohKioKwcHB2LdvH44fP47hw4cr95OSkuDp6QkHBwdERETg22+/xcyZM7F69WqlTFhYGPr06QMfHx9cuHABXbt2RdeuXXHlypV3agsRERG9v0roMni7du3Qrl27XO8JIbB48WJMnToVXbp0AQBs3LgRNjY2CAoKQu/evXHt2jUcPHgQZ8+eRaNGjQAAy5YtQ/v27fHdd9/Bzs4OmzdvRlpaGtatWwcDAwPUqlULkZGRWLRokZJ8LVmyBG3btoW/vz8AYM6cOQgODsby5cuxatWqt2oLERERvd8K7ZyqmJgYxMXFwcPDQ7lmaWkJNzc3hIeHAwDCw8NhZWWlJFQA4OHhAT09PZw+fVop06xZMxgYGChlvLy8cOPGDTx69Egpkz1OVpmsOG/Tltw8f/4cSUlJag8iAsaOHYu+ffuib9++GDt2rK6bQ0QkRaFNquLi4gAANjY2atdtbGyUe3FxcShXrpza/RIlSsDa2lqtTG51ZI/xujLZ7+fVltzMmzcPlpaWysPe3j6PfzXR+yEhIQHx8fGIj49HQkKCrptDRO+J0sYWKPN/j9LGFtLr1+nwX3E3ZcoU+Pn5Ka+TkpKYWBEREenIzGafarX+QttTZWtrCwCIj49Xux4fH6/cs7W1xYMHD9Tup6en4+HDh2plcqsje4zXlcl+P6+25MbQ0BAWFhZqDyIiIiqeCm1S5ejoCFtbW4SEhCjXkpKScPr0abi7uwMA3N3dkZiYiIiICKXMkSNHkJmZCTc3N6XM8ePH8eLFC6VMcHAwqlevjlKlSillssfJKpMV523aog2cd0JERFR06HT4Lzk5GdHR0crrmJgYREZGwtraGhUrVsS4cePw9ddfo2rVqnB0dMS0adNgZ2eHrl27AgCcnZ3Rtm1bDBs2DKtWrcKLFy8watQo9O7dG3Z2dgCAvn37YtasWfDx8cGkSZNw5coVLFmyBD/88IMSd+zYsWjevDm+//57dOjQAdu2bcO5c+eUbRdUKlWebdGGrHknREREVPjpNKk6d+4cWrZsqbzOmn80cOBABAQEYOLEiUhJScHw4cORmJiIjz76CAcPHoSRkZHyNZs3b8aoUaPQunVr6OnpwdvbG0uXLlXuW1pa4vDhw/D19UXDhg1RpkwZTJ8+XW0vqw8//BBbtmzB1KlT8eWXX6Jq1aoICgpC7dq1lTJv0xYiIiJ6f+k0qWrRogWEEK+9r1KpMHv2bMyePfu1ZaytrbFly5Y3xqlTpw7++OOPN5bp2bMnevbsqVFbiIiI6P1VaOdUERERERUlTKqIiIiIJGBSRURERCQBkyoiIiIiCbijOhFJ1S5odJ5l0lIfKs/jUx/m+TW/dV2mcbuIipOxY8cqRzyVLVsWS5Ys0XGLCGBSRUREVORwH8PCicN/RERERBIwqSIiIiKSgMN/RERE76G/vovLs0xGUoba87y+5oMvbDVuV1HGnioiIiIiCZhUEREREUnApIqIiIhIAiZVRERERBJwojoRFUkddn+fZ5lnqUnK8/jUpDy/Zn/3CRq3i4jeX+ypIiIiIpKASRURERGRBBz+I6ICpzIrAZHtORFRccB3Mx1KWPnzG+9nPElRe55X+bIjP5XSLiJtK9nDUddNICKSjsN/RERERBIwqSIiIiKSgMN/RERFxNixY5GQkAAAKFu2LJYsWaLjFhFRdkyqiIiKiISEBMTHx+u6GUT0Ghz+IyIiIpKASRURERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgm4+o+ISEOytjrouGPnG+8/S0lVnj9ISc2z/L6ePfLVDiLKHyZVREQa4lYHJNOZ9Q/yLPM8OUPteV5f4zq4nMbtorxx+I+IiIhIAiZVRERERBIwqSIiIiKSgEkVERERkQRMqoiIiIgk4Oo/IqIiQmVmmutzIiocmFQVYqWNTXJ9TkTvJ8Ou3Qokjqx9t4jeN0yqCrFZLTvouglE9B7ivltE+cOkiiif+Nc8ERFlx6SKKJ/41zwREWXHpIqI6A067lqXZ5lnqcnK8wepyXl+zT7vIRq3i6gglDKyzvU55Y5JFREREeVqapMZum5CkcKkioodznUiIiJdYFJFxQ7nOhERkS5wR3UiIiIiCdhTRUTFlsrMCCLbcyIibWJSRfQa29Z7vfF+SnJ6tufxeZYHgN6DD71zOzhHLP8Mu7vruglE9B5hUkVUyHGOGBFR0cCkitgTQkREJAGTKmJPCBERkQRMqoiINKQyNcn1eWHVdefvb7yfnPpMef4g9Vme5QEgqIeHxu0iKuqYVBERaciwu6eum0BEhQCTKipyQtZ0eOP9Z8nPsj2Pz7M8ALQeul/jdhER0fuNSRVRPmUf5SkCIz5ERKRlTKqI8qmzl5xfn2Wb37y/VVJKerbn8XmWB4DR/d59PywiItIMk6r3QNzKWW+8n/EkUe15XuUBwHYkTy4nIiLKjmf/EREREUnAnioiIqIixtKkdK7PSbeYVBERERUxI7xm6roJlAsO/xERERFJwJ4qokLO2CT350REVLgU6p6qmTNnQqVSqT1q1Kih3H/27Bl8fX1RunRpmJmZwdvbO8cZdrGxsejQoQNMTExQrlw5+Pv7Iz09Xa3MsWPH0KBBAxgaGqJKlSoICAjI0ZYVK1agUqVKMDIygpubG86cOaOVf7MuWBsboozJy4e1saGum6Mxc2MVLE0BS9OXz4u6Zh1LwOuTl49mHfl3EBFRYVXo36Fr1aqF33///+dOlSjx/5s8fvx47N+/Hzt27IClpSVGjRqF7t274+TJkwCAjIwMdOjQAba2tggLC8P9+/cxYMAAlCxZEnPnzgUAxMTEoEOHDhgxYgQ2b96MkJAQDB06FOXLl4eX18v9gAIDA+Hn54dVq1bBzc0NixcvhpeXF27cuIFy5coV4HdDO2a0rKfrJkg1oE3RTwyJiKjoKdQ9VcDLJMrW1lZ5lClTBgDw+PFjrF27FosWLUKrVq3QsGFDrF+/HmFhYTh16hQA4PDhw7h69Sp+/vln1KtXD+3atcOcOXOwYsUKpKWlAQBWrVoFR0dHfP/993B2dsaoUaPQo0cP/PDDD0obFi1ahGHDhmHw4MGoWbMmVq1aBRMTE6xbt67gvyFERERUKBX6pOrWrVuws7ODk5MT+vXrh9jYWABAREQEXrx4AQ+P/38yeo0aNVCxYkWEh4cDAMLDw+Hi4gIbGxuljJeXF5KSkhAVFaWUyV5HVpmsOtLS0hAREaFWRk9PDx4eHkqZ13n+/DmSkpLUHkREhZ2eqTlU5pZQmVtCz9Rc180hKjIK9fCfm5sbAgICUL16ddy/fx+zZs3Cxx9/jCtXriAuLg4GBgawsrJS+xobGxvExcUBAOLi4tQSqqz7WffeVCYpKQlPnz7Fo0ePkJGRkWuZ69evv7H98+bNw6xZee9OTkRUmJh066/rJhAVSYU6qWrXrp3yvE6dOnBzc4ODgwO2b98OY2NjHbbs7UyZMgV+fn7K66SkJNjb2+uwRURERKQthX74LzsrKytUq1YN0dHRsLW1RVpaGhITE9XKxMfHw9bWFgBga2ubYzVg1uu8ylhYWMDY2BhlypSBvr5+rmWy6ngdQ0NDWFhYqD2IiIioeCpSSVVycjJu376N8uXLo2HDhihZsiRCQkKU+zdu3EBsbCzc3d0BAO7u7rh8+TIePHiglAkODoaFhQVq1qyplMleR1aZrDoMDAzQsGFDtTKZmZkICQlRyhAREREV6qTqiy++QGhoKO7evYuwsDB069YN+vr66NOnDywtLeHj4wM/Pz8cPXoUERERGDx4MNzd3dGkSRMAgKenJ2rWrIn+/fvj4sWLOHToEKZOnQpfX18YGr5cdj9ixAjcuXMHEydOxPXr1/Hjjz9i+/btGD9+vNIOPz8//PTTT9iwYQOuXbuGkSNHIiUlBYMHD9bJ94WIiIgKn0I9p+qvv/5Cnz598N9//6Fs2bL46KOPcOrUKZQtWxYA8MMPP0BPTw/e3t54/vw5vLy88OOPPypfr6+vj3379mHkyJFwd3eHqakpBg4ciNmzZytlHB0dsX//fowfPx5LlizBBx98gDVr1ih7VAFAr169kJCQgOnTpyMuLg716tXDwYMHc0xeJyIiovdXoU6qtm3b9sb7RkZGWLFiBVasWPHaMg4ODjhw4MAb62nRogUuXLjwxjKjRo3CqFGj3liGiIiI3l+FeviPiIiIqKgo1D1VREREVPyNHTsWCQkJAICyZctiyZIlOm5R/jCpIiIiIp1KSEjIsXVRUcThPyIiIiIJmFQRERERScCkioiIiEgCJlVEREREEjCpIiIiIpKAq/+owBSXJbNERPT24n+IzLNMRlKa2vO8vsZmfD3NGqUlTKqowBSXJbNERES54fAfERERkQRMqoiIiIgkYFJFREREJAGTKiIiIiIJmFQRERERScDVf0RERKRT1kaWuT4vaphUkTRRP3Z+4/0XT1KzPX+QZ3kAqPX5rxq3i4iICreZTUfruglScPiPiIiISAImVUREREQSMKkiIiIikoBJFREREZEETKqIiIiIJODqPyowVsaqXJ8TEREVB0yqqMCMa2Gs6yYQERFpDYf/iIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISAImVUREREQSMKkiIiIikoBJFREREZEE3PyTiBRjx45FQkICAKBs2bJYsmSJjltERFR0MKkiIkVCQgLi4+N13QwioiKJw39EREREErCnioiISAIOnxOTKiIiIgk4fE4c/iMiIiKSgEkVERERkQRMqoiIiIgk4JwqovfEFzvb5lnmUeqLbM/j8/ya73oc1LhdRETFBZMqIiKitxC049833k9NyVR7nld5AOjas4zG7aLCg8N/RERERBKwp4qIiHSC+zpRccOkioiIdIL7OlFxw6SKiIhIAjMz61yf0/uDSRURKUqaqgCIbM+J6G316Dpb100gHWNSRUSKat34lkDy9Nx16Y33E1PTlOcJqWl5lgeAHd51NG4XkbZw9R8RERGRBPyzlIiIdELP1CrX50RFFZMqIiLSCYvuY3TdBCKpOPxHREREJAGTKiIiIiIJmFQRERERScCkioiIiEgCJlVEREREEjCpIiIiIpKASRURERGRBEyqiIiIiCRgUvWOVqxYgUqVKsHIyAhubm44c+aMrptEREREhQCTqncQGBgIPz8/zJgxA+fPn0fdunXh5eWFBw8e6LppREREpGNMqt7BokWLMGzYMAwePBg1a9bEqlWrYGJignXr1um6aURERKRjPPvvLaWlpSEiIgJTpkxRrunp6cHDwwPh4eG5fs3z58/x/Plz5fXjx48BAElJSQCAJ0+fSm2j4f/V+6onT59JjQMAJrnESn76QnqcpFzipBRQnNSn6QUS52lqwcR5XkBx0lPTCiTOi1T5P9e5x5H7e/q6OC9jpRZQnBSpcV4X60VqcoHEeZb6RAtxTHNcS9VKHIMc15KfaiOOUY5rT55pI45JLnHk/xwY5/Jz8OSp/J9ro/+Lk/VzJ4R48xcIeit///23ACDCwsLUrvv7+wtXV9dcv2bGjBkCAB988MEHH3zwUQwe9+7de2OuwJ4qLZoyZQr8/PyU15mZmXj48CFKly4NlUr1VnUkJSXB3t4e9+7dg4WFhbaayjiMU+CxGIdxGKdoxGIcQAiBJ0+ewM7O7o3lmFS9pTJlykBfXx/x8fFq1+Pj42Fra5vr1xgaGsLQ0FDtmpWVVb7iW1hYaP0XlHEYRxexGIdxGKdoxHrf41haWuZZhhPV35KBgQEaNmyIkJAQ5VpmZiZCQkLg7u6uw5YRERFRYcCeqnfg5+eHgQMHolGjRnB1dcXixYuRkpKCwYMH67ppREREpGNMqt5Br169kJCQgOnTpyMuLg716tXDwYMHYWNjo7WYhoaGmDFjRo5hRMZhnKIei3EYh3GKRizGeXsqIfJaH0hEREREeeGcKiIiIiIJmFQRERERScCkioiIiEgCJlVEREREEjCpIq0RQiA2NhbPnsk/o42IiKiw4eo/0prMzEwYGRkhKioKVatW1XVziKT766+/8OuvvyI2NhZpaeoHSS9atEhHrSo6sj5+3vbYLipY0dHRuH37Npo1awZjY2MIIfh/lQfuU1UIbdq0CatWrUJMTAzCw8Ph4OCAxYsXw9HREV26dJESQwiB6OhopKWloXr16ihRQv6Pgp6eHqpWrYr//vtP60nVwYMHYWZmho8++ggAsGLFCvz000+oWbMmVqxYgVKlSmk1fkH466+/AAAffPCBxnVdunQJtWvXhp6eHi5duvTGsnXq1NE4XnEUEhKCzp07w8nJCdevX0ft2rVx9+5dCCHQoEEDXTfvrfn5+WHOnDkwNTVVO6s0N7ISxY0bN+Lbb7/FrVu3AADVqlWDv78/+vfvL6X+LC9evMBnn32GadOmwdHRUWrdupCUlJTrdZVKBUNDQxgYGEiJ899//6FXr144cuQIVCoVbt26BScnJ/j4+KBUqVL4/vvvpcQpjphUFTIrV67E9OnTMW7cOHzzzTfIyMgA8PLMwMWLF0tJqmJiYtC5c2dcvXoVwMsP6V27dqFRo0Ya1/2q+fPnw9/fHytXrkTt2rWl15/F398fCxYsAABcvnwZEyZMgJ+fH44ePQo/Pz+sX79e4xjdu3d/67K7d+/WOB7wsrfv66+/xvfff4/k5GQAgLm5OSZMmICvvvoKenr5G8GvV68e4uLiUK5cOdSrVw8qlQrZO62zXqtUKuVnUBNLly59q3JjxozROBYA6Ovr4/79+yhXrpza9f/++w/lypWT8m+aMmUKvvjiC8yaNQvm5ubYtWsXypUrh379+qFt27Ya119Qie+FCxfw4sUL5fnryOqhWLRoEaZNm4ZRo0ahadOmAIATJ05gxIgR+PfffzF+/HgpcQCgZMmS2LVrF6ZNmyatzjd59uwZLl26hAcPHiAzM1PtXufOnTWu38rK6o3/Dx988AEGDRqEGTNm5Pu9AQDGjx+PEiVKIDY2Fs7Ozsr1Xr16wc/Pr8glVUlJScoZfwcOHEB6erpyT19fHx06dJAWi8N/hUzNmjUxd+5cdO3aFebm5rh48SKcnJxw5coVtGjRAv/++6/GMXr06IGoqChMnz4dRkZG+O677/Ds2TNERERI+BeoK1WqFFJTU5Geng4DAwMYGxur3X/48KGUOGZmZrhy5QoqVaqEmTNn4sqVK9i5cyfOnz+P9u3bIy4uTuMY73IckYwkDnj5wb127VrMmjVL7QNo5syZGDZsGL755pt81fvnn3+iYsWKUKlU+PPPP99Y1sHBIV8xsnubXgKVSoU7d+5oHAt42UualTRm988//6By5cp4+vSpxjHMzc0RGRmJypUro1SpUjhx4gRq1aqFixcvokuXLrh7965G9Wf/N+jp6Wk98S0ojo6OmDVrFgYMGKB2fcOGDZg5cyZiYmKkxhs4cCDq1asnNVnLzcGDBzFgwIBc36Nl/R9t3LgRX331FQYNGgRXV1cAwJkzZ7BhwwZMnToVCQkJ+O677+Dv748vv/wy33FsbW1x6NAh1K1bV+1z6M6dO6hTp47yB54MWT/br6Pp923fvn2YNm2a8geDubk5UlJSlPsqlQqBgYHo0aOHRnGysKeqkImJiUH9+vVzXDc0NFT7QdDEiRMnsHPnTmWorEmTJvjggw+QkpICU1NTKTGyLF68WGp9r2NgYIDU1FQAwO+//668YVtbW7+2y/xdyUqU3sWGDRuwZs0atb9y69SpgwoVKuDzzz/Pd1KVPVGSkTTlRfYH5etk9YipVCqsWbMGZmZmyr2MjAwcP34cNWrUkBLL1NRUmUdVvnx53L59G7Vq1QIAKX/8xMTEoGzZssrz4uL+/fv48MMPc1z/8MMPcf/+fenxqlatitmzZ+PkyZNo2LBhjvc4Wb2jo0ePRs+ePTF9+nStHV22YcMGfP/99/jkk0+Ua506dYKLiwv+97//ISQkBBUrVsQ333yjUVKVkpICExOTHNcfPnwo/WiXPXv2qL1+8eIFLly4gA0bNmDWrFka17969WqMHj1a7Vp0dDScnJwAAAsXLsS6deukJVUQVKg4OzuLoKAgIYQQZmZm4vbt20IIIZYuXSrq168vJYZKpRJxcXFq10xNTcWdO3ek1K8LnTp1El5eXmL27NmiZMmS4q+//hJCCHHo0CFRtWpVHbcu/wwNDcWNGzdyXL9+/bowMjKSEsPe3l70799frFmzRkRHR0upU1cqVaokKlWqJFQqlbC3t1deV6pUSVSrVk14enqKU6dOSYnVpUsXsXr1aiGEEBMmTBBVqlQRX3/9tWjQoIFo3bq1lBgF7enTp2LhwoWiXbt2omHDhqJ+/fpqDxlq1aolvvnmmxzX58yZI2rXri0lRnbZfwZefTg6OkqLY25urvXfHyMjI3Hz5s0c12/evCmMjY2FEELcuXNHeZ5f7dq1E1OnThVCvPwcunPnjsjIyBA9e/YU3t7eGtX9tjZv3iw6d+6scT2VKlUS169fV15n/1wVQohLly6JsmXLahwnC3uqChk/Pz/4+vri2bNnEELgzJkz2Lp1K+bNm4c1a9ZIiaFSqZCcnKw2FKenp4cnT56o9epkjUFr6vbt21i/fj1u376NJUuWoFy5cvjtt99QsWJF5S97TS1fvhyff/45du7ciZUrV6JChQoAgN9++03K/BYAqF+//lvPKzl//ryUmHXr1sXy5ctzzElavnw56tatKyXG3Llzcfz4cSxYsADDhg1DhQoV0Lx5czRv3hwtWrSQssggPDwc//33Hzp27Khc27hxI2bMmIGUlBR07doVy5Yt0+iv4F9//RU3btyAgYEBWrZsid27d2t1gcKiRYuUYZBZs2YhOTkZgYGBqFq1qlZW/hXEAhYfHx8cPnwYPXr0gKurq1ZWes2aNQu9evXC8ePHlSHtkydPIiQkBNu3b5cer6B6+Xr06IFjx46hcuXKWothb2+PtWvXYv78+WrX165dC3t7ewAv5w1q+nO/cOFCtG7dGufOnUNaWhomTpyIqKgoPHz4ECdPntSo7rfVpEkTDB8+XON67t+/r/a+cvToUeV7BbycOvL48WON42ThnKpCaPPmzZg5cyZu374NALCzs8OsWbPg4+Mjpf7cxrBFtqWyQuI8jdDQULRr1w5NmzbF8ePHce3aNTg5OWH+/Pk4d+4cdu7cqXGMgvIuXdEzZsyQEjM0NBQdOnRAxYoV4e7uDuBlgnLv3j0cOHAAH3/8sZQ4We7fv4/Q0FDs27cPgYGByMzMlPJz0K5dO7Ro0QKTJk0C8HIxQYMGDTBo0CA4Ozvj22+/xWeffYaZM2fmO4a+vj7i4uJQtmzZ105UL6peXcBy5coVODk5ISAgABs2bMDRo0elxLG0tMSBAweUZEdbzp8/j0WLFuHatWsAAGdnZ0yYMCHXqQ+ypKWlISYmBpUrV9bKaufU1FT07NkTZcuWhYuLC0qWLKl2X8Yw46+//oqePXuiRo0aaNy4MQDg3LlzuH79Onbu3ImOHTti5cqVuHXrlsaJ/ePHj7F8+XJcvHgRycnJaNCgAXx9fVG+fHmN/x15efr0KaZMmYLffvsNN27c0KguOzs7bNy4ER4eHrneP3z4MAYOHCht6JlJVSGWmpqK5ORk6R8MoaGhb1WuefPmGsdyd3dHz5494efnpzbh8cyZM+jevbuyTYCmYmNj33i/YsWKUuLowt9//40ff/wR169fB/DyA+jzzz+HnZ2dtBipqak4ceIEjh07hqNHj+LChQtwdnZGixYt8MMPP2hcf/ny5bF3715lhelXX32F0NBQnDhxAgCwY8cOzJgxQ1mRmh+2trb46aef0KlTJ+jp6SE+Pl6Zk6RN586dU5KDmjVromHDhtJjFMQClqw427Zt09o2GrrY4iA1NRWjR4/Ghg0bAAA3b96Ek5MTRo8ejQoVKmDy5MlS4qxduxYjRoyAkZERSpcurfaHq8xFGDExMfjf//6HmzdvAgCqV6+Ozz77DJUqVZJSf0ErVaqU2vdKCIEnT57AxMQEP//8s8arJnv37o3U1FT8+uuvud7v2LEjTE1NERgYqFGcLEyq3kNvO3FbxvCfmZkZLl++DEdHR7UPg7t376JGjRrSdlvX9gqS3CQmJmLnzp24ffs2/P39YW1tjfPnz8PGxkYZfiwKPvzwQ7Ukqnnz5mjWrJnUoTMjIyPcunVL6Xb/6KOP0K5dO3z11VcAgLt378LFxQVPnjzJd4yZM2di9uzZbzVkJePn4a+//kKfPn1w8uRJWFlZAXj5M/Hhhx9i27ZtUvYTy2JsbIzr16/DwcFB7ffo1q1bqFOnjpTVjMDL4fKlS5di1apVWlvAYGlpicjIyAJLqsaOHYuTJ09i8eLFaNu2LS5dugQnJyf88ssvmDlz5hu3kXgXtra2GDNmDCZPnqzRdga6kte2HdnJTLoDAgLUfmf19PRQtmxZuLm5SXkPunDhAtzd3dGpUydMnDgR1apVAwDcuHEDCxYswP79+xEWFiZtbznOqSoECnquTl57nWSR8cFjZWWF+/fv53gDvXDhgtTE49U3xqwVJIsWLcr3Crk3uXTpEjw8PGBpaYm7d+9i2LBhsLa2xu7duxEbG4uNGzdKiVOlShV8+umn6Nevn9Y2UL1+/TpMTU1Ro0YN1KhRA87OztLnItnY2CAmJgb29vZIS0vD+fPn1YZTnzx5kmO45F3NnDkTvXv3RnR0NDp37oz169cryY42DB06FC9evMC1a9dQvXp1AC/fqAcPHoyhQ4fi4MGD0mI5OjoiMjIyR6Jz8OBBtX2ENNWoUSM8e/YMTk5OMDExyfF/ImMLlK5duyIoKEjrWxxkCQoKQmBgIJo0aaL2vlerVi1lioUMaWlp6NWrl9YTqsTERJw5cybXvbBe3abiXeS2X11uZG/hMWjQIGl15aZ+/foIDAzE0KFDc+wfWKpUKWzbtk3qZr1MqgqBrl27Fmi87PMvhBBo37491qxZo5Xeld69e2PSpEnYsWMHVCoVMjMzcfLkSXzxxRcavQG8KrdJ240aNYKdnR2+/fbbd9q48234+flh0KBBWLhwIczNzZXr7du3R9++faXF8fX1xZYtWzB79mw0bNgQn376KXr16gVbW1tpMf777z9cvnwZx44dw6FDh/DVV1/BwMAAzZs3R8uWLTFs2DCNY7Rv3x6TJ0/GggULEBQUBBMTE7X5YJcuXZIywTcrMZwxYwZ69uyZ67JwWUJDQxEWFqYkVMDLoZhly5ZJn+tWEAtYAKBPnz74+++/MXfuXNjY2GhlonpBbXGQJSEhIdcpFCkpKVL/fQMHDkRgYKBGWxnkZe/evejXrx+Sk5NhYWGRY4hRk/dUXW3bURCnYXTp0gVt2rTBoUOHlF38q1atCk9PTzx69AjDhw/H6tWrNY4DgFsqUM4lpjI9f/5cDB06VJQoUUKoVCpRsmRJoaenJz799FORnp6ulZjZ3bp1S5iYmEiv18LCQlk+nf37d/fuXWFoaCg93o0bN8T06dNF1apVRYkSJUSbNm3Ehg0bpMfJzMwUZ8+eFQMHDhQlSpQQenp6UupNSEgQH3/8sVCpVMLc3Fzs2rVL7X6rVq3El19+KSVWdg8ePBB//PGH+OOPP8SDBw+k1l21alVx+vTpHNdPnz4tKleuLDWWEEL8/PPPokqVKkKlUgmVSiUqVKgg1qxZIzWGsbGxiIyMlFrnqwpqi4MsH3/8sVi6dKkQ4v9vDyCEEKNGjRJeXl7S4owePVpYWlqKZs2aiVGjRonx48erPWSoWrWqGDt2rEhJSZFSX2FQu3ZtsX//fiHEy+0NDAwMxJQpU0STJk3EoEGDtB4/MjJS2vucEEJwTlUhVRCTX7Nkn6OhLffu3cPly5eRnJyM+vXrSx/KenWemBAC9+/fx8yZM3H9+nVERkZKjVeuXDkcOnQI9evXV/v+BQcHY8iQIbh3757UeNmdOnUKI0eOxKVLl6R0w58/fx7Hjh3DsWPHcOLECTx58gQuLi7K/CpZy/WBlyuKzMzMoK+vr3b94cOHMDc313gIMEtqaipGjRqFTZs2Kd8jfX19DBgwAMuWLZPSg/XLL79g7ty5WLFihTIB/9y5cxg9ejQmTZqktR5obS1gAYAGDRrgxx9/RJMmTaTXrSsnTpxAu3bt8OmnnyIgIACfffYZrl69irCwMISGhkp7b23ZsuVr76lUKhw5ckTjGKamprh8+bJW36sBvHZSt0qlgpGREapUqSJtTlxBnIbxJhcvXkSDBg2kDWly+K+QKcjJrwXJ3t5ebW8Q2XKbJyaEgL29PbZt2yY9XufOnTF79mxlXx2VSoXY2FhMmjQJ3t7e0uMBL4+j2LJlCwIDA5GUlISePXtKqdfV1RX169dH8+bNMWzYMDRr1gyWlpZS6s4yZMiQtyq3bt06KfHGjx+P0NBQ/Prrr2rH+4wZMwYTJkzAypUr81XvqyuVUlJS4ObmpizRT09PR4kSJTBkyBCpSdW6devQsmVLODo6wsTERGvDmvPnz8eECRPwzTff5LotgKy96wDtb3GQ5aOPPkJkZCTmz58PFxcXHD58GA0aNEB4eDhcXFykxZG1rcWbeHl54dy5c1pPqrp27Zrr/KrsRyN99NFHCAoK0nh4riBOwyhI7KkqZNq2bYvExERs2LAhx+RXCwsLqZNfs5ibm+PSpUtaWY3j7e0NV1dXZX+iLAsXLsTZs2exY8cOKXFe3SYiawVJlSpVtPKG/fjxY/To0QNnz55FcnIy7OzsEBcXB3d3dxw4cEDacT83b97E5s2bsXXrVsTExKBVq1bo168funfvrnYEiyayHzaqLXp6enBwcED9+vXfOBH21SMr8qtMmTLYuXMnWrRooXb96NGj+OSTT5CQkJCverOW5b+NgQMH5itGbqpWrYo7d+7k2Ji1SpUq0mIAUCZZv24fOxl/zRfUFge6lLVVjOw/gteuXYvZs2dj8ODBuSa9Mg5tBoCQkBB89dVX+Oabb9TOGJw2bRqmTp0KS0tLfPbZZ3Bzc8PatWs1itW5c2ekpaWhadOmmDNnDmJiYlChQgUcPnwYo0aNUraO0BbZPVVMqgoZY2NjhIWF5dgELyIiAh9//LGS0Wvi1Unbe/fuRatWrXIkAq+ulMiPsmXL4siRIzn+Irx8+TI8PDwQHx+vcQxdOnnypNrmeK/bYC6/9PT00LhxY/Tt2xe9e/fW2pli2ubr64utW7fCwcEBgwcPxqeffgpra2utxTMxMUFERESOlXFRUVFwdXWVdo5mQfr7779x7NgxHD9+HKGhobh16xbKly+PFi1a4Oeff5YSI6897GTsXVdQWxxkadWqFZo3b55jQ95Hjx7B29tbyrAcAGRmZuLrr7/G999/r+y0b25ujgkTJuCrr76SsirwTXXIXJVXu3ZtrF69OscZjSdPnsTw4cMRFRWF33//HUOGDMlzj8C8xMbG4vPPP8e9e/cwZswYZZPr8ePHIyMjI8dpEu8qr0VKiYmJCA0NZVJVXFWrVg0///yz8tdBljNnzqBv376Ijo7WOMbgwYPfqpyMA4SNjY0RGRmptkIKeLmMv379+tL219mwYQPKlCmDDh06AAAmTpyI1atXo2bNmsqHuSyZmZkICAjA7t27cffuXahUKjg6OqJHjx7o37+/1BVFt27d0tpWClkKao+v58+fY/fu3Vi3bh3CwsLQoUMH+Pj4wNPTU/oqs9atW6N06dLYuHEjjIyMALzcpXngwIF4+PAhfv/993zVm71XL6+hCW31/qWmpuKPP/7A1q1bsXnzZgghkJ6erpVY2uDg4KBscZB9PmJ0dDQaNGggfchHT08PpUuXRtOmTbF582blj8f4+HjY2dlJ+/meMmUK1q5di1mzZqkNOc+cORPDhg3TytYu2mJsbIyzZ8+idu3aatcvX74MV1dXPH36FH/++SecnZ2l/KGvTQX5eQeAq/8Km6CgIOHq6irOnj2rXDt79qxo0qSJ2LNnj+4alk+NGzcWs2bNynF9xowZokGDBtLiVKtWTYSEhAghhAgLCxPGxsbif//7n+jUqZPo1q2btDiZmZmiQ4cOQqVSiXr16onevXuLXr16iTp16giVSiW6dOkiLVZ2586dE5s2bRKbNm0SERERUusOCgpSe+zYsUN8+eWXWlldluXu3bti5syZwsnJSVSsWFE8efJEav2XL18WdnZ2onTp0qJVq1aiVatWonTp0qJChQriypUr+a5XT09PxMfHCyFeHkyup6eX45F1XaZDhw6JKVOmCHd3d2FkZCTq168vxo0bJ4KCgsTDhw+lxnr06JH47rvvhI+Pj/Dx8RGLFi0SiYmJ0uo3NjZWVstmXzkbGRkpLCwspMXJolKpRGRkpHBzcxO1a9cWMTExQggh4uLipP4/lS9fXvzyyy85rgcFBQk7OztpcQpC06ZNRdu2bdVWzD548EC0bdtWfPzxx0IIIYKDg0W1atU0jpX9dyq7f//9V/rvUUHgRPVCZtCgQUhNTX3t5NfsE35lbMSnbdOmTUP37t1x+/ZttGrVCsDL8fotW7ZIPffv3r17yvySoKAg9OjRA8OHD0fTpk1zzKvRREBAAI4fP46QkJAcq32OHDmCrl27YuPGjdL24Hrw4AF69eqF0NBQtYULLVu2xLZt26Qcw5Lb6r4ePXqgVq1aCAwMlHbmZHZZvWNCCK3sdl+7dm3cunULmzdvVo736dOnD/r166d2kPi7OnLkiDJsWRATk7O0bdsWZcuWxYQJE3DgwAGtbWp67tw5eHl5wdjYWOktz9pAN2uCt6YaNWqE/fv3Y/To0QD+//ytNWvWKOdbyla+fHmEhoZi8ODBaNy4MXbs2CF101Tg5ftxjRo1clyvUaOGtPfq2bNnv/H+9OnTpcRZu3YtunTpgg8++EBZYHTv3j1lmBYAkpOTMXXqVI1jidcMlj1//hwGBgYa11/QOPxXyOhqIqw27d+/H3PnzkVkZCSMjY1Rt25dzJgxA9bW1jm6l/Mr+xYH9evXh5+fH/r374/bt2+jbt26yhwHTXl6eqJVq1avnUw7d+5chIaG4tChQ1Li9erVC3fu3MHGjRuVD4GrV69i4MCBqFKlCrZu3SolTm7u3LmDOnXqSPveZR/+O3HiBDp27IjBgwejbdu2RfJYj4K0ePFiHD9+HMePH4ehoaEyUb1FixbKsRsyfPzxx6hSpQp++ukntT/qhg4dijt37uD48eP5rvvKlSuoXbs2Tp48ibZt2+LTTz/F+vXrMWLECK1scZDl1cO1v/76a3z99deYNGkSvv76a2lJvZubG9zc3HLMARo9ejTOnj2LU6dOaRzj1bm2L168QExMDEqUKIHKlStLOXEjS2ZmJg4fPqx2xmCbNm2k/a5mfZ/Gjx+POXPmqC28ycjIwPHjx3H37l3pc+y0TrcdZfS+efz4sVi1apVo3Lix1K7dvn37igYNGggfHx9hYmIi/v33XyGEEL/88ouoWbOmtDg2NjbiwoULr71//vx5YWNjIy2ehYWFOHPmTI7rp0+fFpaWltLivCo1NVWMHTtWSve+EEKMHDlSlCpVStSpU0csXrxYJCQkSKn3dQICAsS+ffuU1/7+/sLS0lK4u7uLu3fvSovz6NEjcejQIbFp0yaxYcMGtYe2XLp0SSxbtkx069ZNlCxZUlSoUEFa3UZGRuLatWs5rkdFRQljY2ON6lapVMLV1VWsXr1aXLx4UQwdOlQ0btxYODs7i379+olLly5pVP+b4r46vLRz505hamoq9T3o2LFjwtTUVDg7O4shQ4aIIUOGCGdnZ2FmZiaOHz8uLc6rHj9+LLp16yY2btyotRjakLXhq0qlEvb29mqbwFarVk14enqKU6dO6bqZ74zDf4VQRkYG9uzZo7b5Z5cuXbS6l4u2HT9+HGvXrsWuXbtgZ2eH7t27Y8WKFdLqX7FiBaZOnYp79+5h165dKF26NICXqyb79OkjLc7Dhw/fuALPxsYGjx49khYvMzMz1w0xS5YsmePcr/zK65R4GVatWoWKFSvCyckJoaGhr11lJmPFKfCyxzBrL6rw8HAsX74cixcvxr59+zB+/HgpcbR5ZEhuhBC4cOECjh07hqNHj+LEiRPIzMyUMgScxcLCArGxsTmGse7du6d2HFN+hIaGYv369ZgwYQIyMzPh7e2N7777Ds2aNdOo3rzExMTk+B55e3ujevXqiIiIkBanefPmuHHjBn788UdlyLl79+74/PPPYWdnJy3OqywsLDBr1ix06tQJ/fv3l1ZvaGgovvvuO7XPIX9/f2lHMGUdi9OyZUvs3r1b+nmjusLhv0ImKioKnTt3RlxcnLJi7ubNmyhbtiz27t0rbbisIMTFxSEgIABr165FUlISPvnkE6xatQoXL15EzZo1tRr7yZMn2Lp1K9asWYOIiAhpXfz6+vqIi4t77QeZ7BVFXbp0QWJiIrZu3aq8Mf/999/o168fSpUqJWVfp1eHnGWfEg+8nCv4Niv8ZK3AMTExwfXr11GxYkVMmjQJ9+/fx8aNGxEVFYUWLVrke5+q7KpVq4b27dtj7ty5Wj1jEAA6deqEkydPIikpCXXr1lV2u2/WrJnU+VVjxozBnj178N133ynL6U+ePAl/f394e3tj8eLFGsdISUnB9u3bERAQgD/++ANVqlSBj48PBg4cKPVMy/fJiRMn0KlTJ2l/0P38888YPHgwunfvrraSMSgoCAEBAVLPNy1umFQVMu7u7ihbtiw2bNigfKA9evQIgwYNQkJCAsLCwnTcwrfTqVMnHD9+HB06dEC/fv3Qtm1b6Ovro2TJklpNqnLrEfP29kbjxo2l1K+np4d27drB0NAw1/vPnz/HwYMHpSVV9+7dQ+fOnREVFaU2YbR27dr49ddfi+wO+9pWEHPsCurIEADw9/dH8+bN8fHHH0vf7T67tLQ0+Pv7Y9WqVco2DSVLlsTIkSMxf/781/7c51d0dDTWr1+PTZs2IS4uDm3btn3tESmaOHfuHLZv347Y2FikpaWp3ZPVO7p+/XqYmZnlOOlgx44dSE1NlTIH9tX5WuL/juPatGkTmjdvji1btmgcAwCcnZ0xfPhwjB8/Xu36okWL8NNPPym9V/nl5+eHOXPmwNTUFH5+fm8su2jRIo1iFTQmVYWMsbExzp07h1q1aqldv3LlCho3bixtXydtK1GiBMaMGYORI0eq7bOkjaSqIHvECnzPE7x84/z999+VIQVnZ2fpm4wmJibizJkzePDgQY5hRdnDWAWhX79+yl5oW7duRWxsLEqXLo1ff/0VX375Ja5cuaJxjO7du6N379745JNPJLS4cElNTcXt27cBAJUrV9ZqT1xKSgo2b96MKVOmIDExUfpq0G3btmHAgAHw8vLC4cOH4enpiZs3byI+Ph7dunWT9rtarVo1/O9//8uxKjg0NBTDhw/HjRs3NI7x6qkXWb3KrVq1wpQpUzQeos1iaGiIqKioHDv2R0dHo3bt2nj27JlG9bds2RJ79uyBlZVVgZyZWJCK7iSdYqpatWqIj4/PkVQ9ePBA+pEU2nTixAmsXbsWDRs2hLOzM/r374/evXtLj5O9Ryxrh2Z9fX2sWrVKeixAbrL0tlQqFdq0aYM2bdpopf6CnhtUELQ1xy57L0qHDh3g7++Pq1evavXIkCwpKSkIDQ3NtbdlzJgxUmOZmJhIPRcvN8ePH8e6deuwa9cu6Onp4ZNPPtHK9h1z587FDz/8AF9fX5ibm2PJkiVwdHTEZ599hvLly0uLExsbm+tRXw4ODhrvOp4lax6Sttnb2yMkJCTHZ87vv/8u5QzX7NuRFOTWJAWBPVWFzIEDBzBx4kTMnDlTOSn+1KlTmD17NubPn4+PPvpIKavt89pkSElJQWBgINatW4czZ84gIyMDixYtwpAhQ6T8VVWQPWK6EhISgpCQkFx7kWQcQFyQc4OKurddTi7zyBAAuHDhAtq3b4/U1FSkpKTA2toa//77L0xMTFCuXDncuXNHWixtDpX9888/CAgIQEBAAKKjo/Hhhx/Cx8cHn3zyibTzMl9lamqKqKgoVKpUCaVLl8axY8fg4uKCa9euoVWrVrh//76UOBUrVsTy5ctzJNO//PILfH19lfMAZUpKSsKRI0dQvXp1qfturVy5EuPGjcOQIUPU5tYFBARgyZIl+Oyzz6TFKnZ0s+iQXkelUimP7Ds0v/q6KO40e/36deHv7y9sbW2FkZGR6NSpk8Z1hoeHi6FDhwpzc3Ph6uoqli1bJhISEkSJEiVEVFSUhFbr1syZM4Wenp5wdXUVXbp0EV27dlV7yGBiYqLsal2caHtn8ILUvHlzMWzYMJGRkaHsQh4bGyuaNWsmdu3aJS3O1q1bRcmSJUXHjh2FgYGB6Nixo6hWrZqwtLQUgwYN0qjutm3bihIlSghbW1sxceJEcf36dUmtfrMKFSoo2zW4uLiILVu2CCFenrwgcwf3iRMnCgcHB3HkyBGRnp4u0tPTRUhIiHBwcBATJkyQEqNnz55i2bJlQoiX255UrVpVlCxZUpQoUULs3LlTSowsu3fvFk2bNhXW1tbC2tpaNG3aVAQFBUmpu1u3buLx48fK8zc9+vfvL+bPn19kfnfZU1XIvOlA00uXLqFOnTrKaxmHm+pCRkYG9u7di3Xr1kmblKrtHjFdKV++PBYuXCh1qfSriuPcoNx2Bj979iyePn0qbWfw3CQmJmplt3MrKyucPn0a1atXh5WVFcLDw+Hs7IzTp09j4MCBynw7TdWpUwefffaZMlR28eJFtaGyWbNm5bvuzp07w8fHBx07doS+vr6U9r6Nvn37olGjRsrk6GXLlqFLly4IDg5G/fr1paygBV5O8u/fvz927NihbH+TmZmJAQMGYNWqVVJ2B7e1tcWhQ4dQt25dbNmyBTNmzMDFixexYcMGrF69ushslDl48GAsXboU5ubmec5Tff78OcLDw+Hi4qKVRQyyMakq5LS1NUBxduPGDaxduxabNm1CYmIi2rRpUyR+GXNTunRpnDlzBpUrV5Zab/bvR0JCAmbPno3BgwcXyNyggqDNncGzLFiwAJUqVUKvXr0AAD179sSuXbtQvnx5HDhwAHXr1tU4RpayZcsiLCwMVatWRbVq1bBs2TJ4eXnh+vXraNiwIVJSUqTEKaihsoL08OFDPHv2DHZ2dsjMzMTChQuV7+UXX3whdV4V8HILnIsXL8LY2BguLi5SD3M3NjbGzZs3YW9vjwEDBsDOzg7z589HbGwsatasKe30gywRERHKSr9atWrl2NG9oFy9ehWNGzeW9nOuVbrtKKPXCQ0NFQMGDBCmpqaiatWqYtKkSbnurE2vl56eLvbs2SNlmFFXJk6cKGbPni293uzDzG96FMVhZiG0uzN4lkqVKomTJ08KIYQ4fPiwsLKyEocOHRI+Pj6iTZs2UmJkadOmjdi8ebMQQoihQ4cKV1dX8fPPPwsvLy/h6uoqLU5BDZXp2tOnT8V3330n9fSDglC1alURGBgokpOTRdmyZZVD5CMjI0Xp0qWlxYmPjxctW7YUKpVKlCpVSpQqVUqoVCrRqlUrtUOWC0p6erqIjIws8Lj5wdV/hUhuWwM8f/4cQUFBxWLCdUHT19dH165d0bVrV103Jd+ePXuG1atX4/fff0edOnVy9CLldw8XWbuxF1ba3Bk8S1xcnLISat++ffjkk0/g6emJSpUqwc3NTUqMLHPnzsWTJ08AAN988w0GDBigLM6QsVghS7NmzRAcHAwXFxf07NkTY8eOxZEjRxAcHIzWrVtLi1MQnj9/jpkzZyI4OBgGBgaYOHEiunbtivXr12Pq1KnQ19fPsQ+Tpv766y/8+uuvuU7yl7Hf0rhx49CvXz+YmZnBwcFBOSz++PHjUldrjh49Gk+ePEFUVFSOM0fHjBkj9czRli1bvnFj4CNHjkBfX19qz69W6Tqro5c6duwoLCwsRJ8+fcS+fftEenq6EEIUmwnXlD8tWrR47aNly5Ya1R0SEiKcnZ2VCaPZJSYmipo1a2r1zDJtGj16tPjggw/Etm3bRGxsrIiNjRVbt24VH3zwgRg7dqyUGOXLl1d6qqpVqya2b98uhHi5IMPc3FxKjIL233//ib///lsIIURGRoaYN2+e6NSpk/Dz8xMPHz7UcevezcSJE4WlpaXw9vYW5cuXFyVKlBDDhg0TLi4uYuvWrcp7rCy///67MDExEbVr1xYlSpQQ9erVE1ZWVsLS0lLj39Xszp49K3bv3i2ePHmiXNu3b584ceKEtBgFeebouHHj1B6+vr6iadOmwtLSUowZM0ZqrILApKqQ0NfXF+PHjxc3b95Uu86kirSlU6dOYtGiRa+9v2TJEmkrDAva8+fPxZgxY4SBgYGyatbQ0FCMGzdOPHv2TEoMX19f4eDgIDw8PETp0qWVD7mtW7eK+vXrS4lB+efo6Ch++eUXIYQQly9fFiqVSgwePFhkZmZqJV7jxo3F9OnThRBCWaH55MkT0blzZ/Hjjz9qJaa2mJmZ5Xpw/Pnz5wvsD4YZM2ZIWzVZkDhRvZA4deoU1q5di8DAQLXNMsuXL19s9lui/IuOjsbt27fRrFkzGBsbQwjxVmfpvYmDgwMOHjz42v1trl+/Dk9PT2kbFxaUjIwMnDx5Ei4uLjA0NNTazuAvXrzAkiVLcO/ePQwaNEiZxPvDDz/A3NwcQ4cOlRbL0dHxjf/fmuxTlZSUpOx5l5SU9MayRWFvvCwGBgaIiYlBhQoVALyc5H3mzBmtbWpqbm6OyMhIVK5cGaVKlcKJEydQq1YtXLx4EV26dMHdu3c1jiGEwM6dO3H06NFc962TdeROQZw5mpfo6Gi4urri4cOHWo8lE+dUFRJNmjRBkyZNsHjxYmVrAD8/P2RmZiI4OBj29vZFemsAyp///vsPn3zyCY4ePQqVSoVbt27ByckJPj4+KFWqFL7//vt81x0fH59jjlZ2JUqUkHLwcEHT19eHp6cnrl27BkdHR619iJYsWRJffPFFjuuy5+kAL+fSZPfixQtcuHABBw8ehL+/v0Z1lypVCvfv30e5cuVgZWWVa/KWlcQXpdXHGRkZatsYlChRAmZmZlqLZ2pqqsyjKl++PG7fvq2cjPHvv/9KiTFu3DjlKBwbGxuN/7B6naxNTCtVqqTMG4yNjYWLiwt+/vlnrcR8VXh4OIyMjAoklkxMqgoZU1NTDBkyBEOGDFG2Bpg/fz4mT55cpLcGoPwZP348SpYsidjYWLUepV69esHPz0+jpKpChQq4cuXKa48/unTpkvTl5gWldu3auHPnTq7Hhsh0+/ZtLF68WFl2XrNmTYwbN076Ictjx47N9fqKFStw7tw5jeo+cuQIrK2tAbz5yJCiNqghhMCgQYOUQ6CfPXuGESNG5Ni5XVbvTpMmTXDixAk4Ozujffv2mDBhAi5fvozdu3crp2NoatOmTdi9ezfat28vpb7Xsbe3x/nz5xESEqL8bGvjzFHg5T552Yn/OyT63LlzmDZtmvR42sbhvyJAG5tlUtGQfbO/rM0YnZyccOfOHdSpU0ejfWlGjx6NY8eO4ezZszn+Inz69ClcXV3RsmVLLF26VNN/RoE7ePAgpkyZgjlz5qBhw4Y5PkhlDGMdOnQInTt3Rr169dC0aVMAL4/yuHjxIvbu3au1sxqzu3PnDurVq5fnsF1epk2bhhkzZih7er0qNjYWPj4+CA4O1ihOQSrow8/v3LmD5ORk1KlTBykpKZgwYYKyH9aiRYuk7Ffl6OiI3377LceqVlmePn2KkJAQdOzYEQAwZcoUPH/+XLlfokQJzJ49W0oP0p07d1CpUqUc5z1mPyTa09NT4zgFTleTuYgob2ZmZsrihazJr0K8XAFkbW2tUd1xcXHCzs5O2NvbiwULFoigoCARFBQk5s+fL+zt7YWdnZ2Ii4vT+N+gC7kd9yT7iKd69eqJSZMm5bg+adKkApuovmDBAuHg4KBxPfb29qJevXri8uXLOe6tWrVKmJubi7Zt22ochzQTEBAgevfuLVJTU7VS/8qVK0XHjh2V12ZmZsLNzU1ZcWxra/vGxS3vQk9PT8THxyuvP/nkkyL7fpMde6qICrH27dujYcOGmDNnDszNzXHp0iU4ODigd+/eyMjIwK5duzSq/88//8TIkSNx6NAhZXhHpVLBy8sLK1as0Prwmba86bgnQM4RT0ZGRrh8+bLaQd7Ayx2169Spg2fPnmkcI0v9+vXV5s8IIRAXF4eEhAT8+OOPGD58uEb1JyUlYdSoUdi+fTtmzJiBSZMm4a+//sKQIUNw9uxZfPvttxrHIM09ffoU3bp1w8mTJ1GpUqUccyLPnz+vUf0ff/wxJk6ciE6dOgGAWu84APz8889YsWIFwsPDNYoDvOyRiouLQ7ly5QC87D2OjIyUPnRe0DiniqgQW7hwIVq3bo1z584hLS0NEydORFRUFB4+fIiTJ09qXL+DgwMOHDiAR48eITo6GkIIVK1aFaVKlZLQet0piHMxy5Yti8jIyBxJVWRkpPJBIUuXLl3UkqqsIZIWLVpIGQqysLDAxo0b4e3tjc8++wyBgYGIiYmBq6urkshTTtbW1rh58ybKlCmDUqVKvXHiuIxVbAMHDkRERAQ+/fRTrUxUj46OVlvYYWRkBD09PeW1q6srfH19pcbMUlz6d5hUERVitWvXxs2bN7F8+XKYm5sjOTkZ3bt3x/Dhw/H1119j9erVUuKUKlUKjRs3llJXYbB+/XqYmZmhZ8+eatd37NiB1NRUDBw4MN91z549G1988QWGDRuG4cOH486dO/jwww8BvJxTtWDBAvj5+WnU/ixZc6XeVF/2LRE01aRJE7i4uCAkJASmpqaYOnUqE6o3yNo+AwAWL16s9Xj79+/HoUOH8NFHH2ml/sTERLU5VK+u/s3MzFS7rwmVSpUjKdTWasYCpcuxRyLKn8jIyCJ7Ll9BqFq1qjhy5EiO68eOHRPVqlXTqO6suSCZmZli0aJFokKFCsr8rQoVKojFixdL22Dy1Tlhrz5kzhHbsmWLsLa2Fq1atRLXr18X/v7+wsDAQIwbN048ffpUSgzSTPXq1cXFixe1Vn+VKlXEzp07X3s/MDBQVK5cWUoslUol2rdvL7p16ya6desmSpQoITw9PZXXWY+ihnOqiIqgixcvokGDBkVq36CCZGRkhOvXr6NSpUpq1+/evQtnZ2c8ffo033W/OhcEgHIun+y95LLPDRNCoH379lizZo2yoWUWTYc7vb29cejQIcybNw+jR49WroeFhSmr6AICAuDu7q5RnOLmXVZdyuhN3L9/P5YtW4ZVq1bl+NmWYezYsfj9998RERGR64rgRo0awcPDA0uWLNE4VkGvziwoTKqIiiAmVW9WsWJFZQPD7H755Rf4+vrir7/+ynfdenp6iI+PR9myZTVt5jt7deKwLE2bNkVAQECO+WHAyw/TyZMnY+XKlTkOCX7f6enpvfWQlYzf1VKlSiE1NRXp6ekwMTHJMVFd03lb8fHxqFevHgwMDDBq1ChUq1YNAHDjxg0sX74c6enpuHDhAmxsbDSKU5xxThURFTt9+vTBmDFjYG5ujmbNmgF42eszduxY9O7dW+P6q1WrlueHaVE6XuOPP/5Qm5CcnbGxMZYsWQJvb+8CblXhl32z1Lt372Ly5MkYNGiQ0qMXHh6ODRs2YN68eVLiaXvelo2NDcLCwjBy5EhMnjxZbUVwmzZt8OOPPzKhygN7qogKoVd3GX5VYmIiQkND2VP1Gmlpaejfvz927NihbGiZmZmJAQMGYNWqVWrHl7wrPT09LF68GJaWlm8sp8lk+NfRVk8Vaa5169YYOnQo+vTpo3Z9y5YtWL16NY4dO6abhuXTw4cPER0dDQCoUqWKsus+vRmTKqJCqLjONyhoN2/exMWLF2FsbAwXFxcpK9lym1NVULL2Kiuq+4cVZyYmJrh48WKu+5bVq1cPqampUuM9e/Ysx3BsUTrwurji8B9RIcRkSY5q1aop80JkKchl36/2WGr7/DrKP3t7e/z0009YuHCh2vU1a9YohxJrKiUlBZMmTcL27dvx33//5bjPnmvdY1JFRMWCn58f5syZA1NT0zz3iVq0aFG+4xRk5/6rQ4yffvppgcWmd/PDDz/A29sbv/32G9zc3AAAZ86cwa1btzQ++SDLxIkTcfToUaxcuRL9+/fHihUr8Pfff+N///sf5s+fLyUGaYbDf0RULLRs2RJ79uyBlZUVWrZs+cay2ScYE8ly7949rFy5EtevXwcAODs7Y8SIEdJ6qipWrIiNGzeiRYsWsLCwwPnz51GlShVs2rQJW7duxYEDB6TEofxjUkVERFQEmJmZ4erVq6hYsSI++OAD7N69G66uroiJiYGLiwuSk5N13cT3Hof/iKjYGDJkSJ5lVCoV1q5dWwCtoffNH3/8gf/973+4c+cOduzYgQoVKmDTpk1wdHSUcrSMk5MTYmJiULFiRdSoUQPbt2+Hq6sr9u7dCysrK83/AaSx3DcmISIqggICAnD06FEkJibi0aNHuT6K0v5RVHTs2rULXl5eMDY2xvnz55Uz8h4/foy5c+dKiTF48GBcvHgRADB58mSsWLECRkZGGD9+PPz9/aXEIM1w+I+Iig1fX19s3boVDg4OGDx4MD799FPur0MFon79+hg/fjwGDBigtp/YhQsX0K5dO8TFxUmP+eeffyIiIgJVqlRBnTp1pNdP7449VURUbKxYsQL379/HxIkTsXfvXtjb2+OTTz7BoUOHCnTVHr1/bty4oezen52lpSUSExM1qjs8PBz79u1Tu5Y1YX3EiBFYvny50jNGusWkioiKFUNDQ/Tp0wfBwcG4evUqatWqhc8//xyVKlXiRF7SGltbW2UH8uxOnDih8Q74s2fPRlRUlPL68uXL8PHxgYeHB6ZMmYK9e/dKOwqHNMOkioiKrawDb4UQ3BiRtGrYsGEYO3YsTp8+DZVKhX/++QebN2/GF198gZEjR2pUd2RkJFq3bq283rZtG9zc3PDTTz9h/PjxWLp0KbZv367pP4Ek4Oo/IipWnj9/jt27d2PdunU4ceIEOnbsiOXLl6Nt27avPTSYSFOTJ09GZmYmWrdujdTUVDRr1gyGhob44osvMHr0aI3qfvTokdpBxqGhoWjXrp3yunHjxrh3755GMUgOTlQnomLj888/x7Zt22Bvb48hQ4agX79+KFOmjK6bRe+RtLQ0REdHIzk5GTVr1oSZmZnGdTo4OGDTpk1o1qwZ0tLSYGVlhb179yq9V5cvX0bz5s25srUQYFJFRMWGnp4eKlasiPr167/xjD6elUeyvM3eaACwbt26fMcYOXIkLl68iAULFiAoKAgbNmzAP//8AwMDAwDA5s2bsXjxYpw9ezbfMUgODv8RUbExYMCAAj3wmCggIAAODg6oX7++1laYzpkzB927d0fz5s1hZmaGDRs2KAkV8DJh8/T01EpsejfsqSIiIsqngtwb7fHjxzAzM4O+vr7a9YcPH8LMzEwt0SLdYFJFRESkgeyLI8LCwtChQwf4+PjA09OTPafvGSZVREREkvz5558ICAjAxo0bkZ6ejqioKCmT1alo4PpiIiIiSbg32vuNSRUREZEGnj9/jq1bt6JNmzaoVq0aLl++jOXLlyM2Npa9VO8Zrv4jIiLKp1f3Rtu6dSv3RnuPcU4VERFRPnFvNMqOPVVERET5xL3RKDv2VBERERFJwInqRERERBIwqSIiIiKSgEkVERERkQRMqoiIiIgkYFJFRMVOXFwcRo8eDScnJxgaGsLe3h6dOnVCSEiIrptGRMUYt1QgomLl7t27aNq0KaysrPDtt9/CxcUFL168wKFDh+Dr64vr16/ruolEVEyxp4qIipXPP/8cKpUKZ86cgbe3N6pVq4ZatWrBz88Pp06dAgDExsaiS5cuMDMzg4WFBT755BPEx8crdcycORP16tXDunXrULFiRZiZmeHzzz9HRkYGFi5cCFtbW5QrVw7ffPONWmyVSoWVK1eiXbt2MDY2hpOTE3bu3KlWZtKkSahWrRpMTEzg5OSEadOm4cWLFzlib9q0CZUqVYKlpSV69+6NJ0+eAAA2btyI0qVL4/nz52r1du3aFf3795f6vSSid8OkioiKjYcPH+LgwYPw9fWFqalpjvtWVlbIzMxEly5d8PDhQ4SGhiI4OBh37txBr1691Mrevn0bv/32Gw4ePIitW7di7dq16NChA/766y+EhoZiwYIFmDp1Kk6fPq32ddOmTYO3tzcuXryIfv36oXfv3rh27Zpy39zcHAEBAbh69SqWLFmCn376CT/88EOO2EFBQdi3bx/27duH0NBQzJ8/HwDQs2dPZGRk4Ndff1XKP3jwAPv378eQIUM0/h4SkQYEEVExcfr0aQFA7N69+7VlDh8+LPT19UVsbKxyLSoqSgAQZ86cEUIIMWPGDGFiYiKSkpKUMl5eXqJSpUoiIyNDuVa9enUxb9485TUAMWLECLV4bm5uYuTIka9tz7fffisaNmyovM4ttr+/v3Bzc1Nejxw5UrRr1055/f333wsnJyeRmZn52jhEpH2cU0VExYZ4iwMirl27Bnt7e9jb2yvXatasCSsrK1y7dg2NGzcGAFSqVAnm5uZKGRsbG+jr60NPT0/t2oMHD9Tqd3d3z/E6MjJSeR0YGIilS5fi9u3bSE5ORnp6OiwsLNS+5tXY5cuXV4szbNgwNG7cGH///TcqVKiAgIAADBo0iMelEOkYh/+IqNioWrUqVCqVlMnoJUuWVHutUqlyvZaZmfnWdYaHh6Nfv35o37499u3bhwsXLuCrr75CWlpanrGzx6lfvz7q1q2LjRs3IiIiAlFRURg0aNBbt4OItINJFREVG9bW1vDy8sKKFSuQkpKS435iYiKcnZ1x79493Lt3T7l+9epVJCYmombNmhq3IWsyfPbXzs7OAICwsDA4ODjgq6++QqNGjVC1alX8+eef+YozdOhQBAQEYP369fDw8FDreSMi3WBSRUTFyooVK5CRkQFXV1fs2rULt27dwrVr17B06VK4u7vDw8MDLi4u6NevH86fP48zZ85gwIABaN68ORo1aqRx/B07dmDdunW4efMmZsyYgTNnzmDUqFEAXvakxcbGYtu2bbh9+zaWLl2KPXv25CtO37598ddff+Gnn37iBHWiQoJJFREVK05OTjh//jxatmyJCRMmoHbt2mjTpg1CQkKwcuVKqFQq/PLLLyhVqhSaNWsGDw8PODk5ITAwUEr8WbNmYdu2bahTpw42btyIrVu3Kj1gnTt3xvjx4zFq1CjUq1cPYWFhmDZtWr7iWFpawtvbG2ZmZujatauUthORZlTibWZ2EhFRnlQqFfbs2VNgSU7r1q1Rq1YtLF26tEDiEdGbcfUfEVER8+jRIxw7dgzHjh3Djz/+qOvmENH/YVJFRFTE1K9fH48ePcKCBQtQvXp1XTeHiP4Ph/+IiIiIJOBEdSIiIiIJmFQRERERScCkioiIiEgCJlVEREREEjCpIiIiIpKASRURERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgn+H3a5Zhw5P9R4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = df['Company'], y= df['Price'])\n",
    "plt.xticks(rotation = \"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = df['TypeName'], y= df['Price'])\n",
    "plt.xticks(rotation = \"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Inches', ylabel='Price'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x= df['Inches'], y = df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float32\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float32\n",
      "dtypes: float32(3), int32(1), object(7)\n",
      "memory usage: 91.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if \"Touchscreen\" in x else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float32\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float32\n",
      " 11  Touchscreen       1303 non-null   int64  \n",
      "dtypes: float32(3), int32(1), int64(1), object(7)\n",
      "memory usage: 101.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Touchscreen'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Touchscreen', ylabel='Price'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Touchscreen'], y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.679688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.523438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.343750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.804688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight          Price  Touchscreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.679688            0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.523438            0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.000000            0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.343750            0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.804688            0   \n",
       "\n",
       "   Ips  \n",
       "0    1  \n",
       "1    0  \n",
       "2    0  \n",
       "3    1  \n",
       "4    1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGYCAYAAABoLxltAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAYeUlEQVR4nO3df6yWdf3H8dc5oAckzkFQzuEsTNZsSFkqFB5t1vJMVGw5KWMjp+ak7FApqcGmNH8UxkodppJOhU2d5R8upaQYblh5BMQ081dt/YBi56AjzlGaB+Sc7x/fea+jpB4E7s+Bx2O7Ns91fe5zv6/N2/P0uu9znZq+vr6+AAAUpLbaAwAAvJVAAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDhDqz3A7ujt7c2mTZsycuTI1NTUVHscAOA96Ovry6uvvprm5ubU1r7zNZJBGSibNm3K+PHjqz0GALAbNm7cmA9+8IPvuGZQBsrIkSOT/P8J1tfXV3kaAOC96O7uzvjx4ys/x9/JoAyUN9/Wqa+vFygAMMi8l49n+JAsAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFGdotQdgYI6c98tqj8A+9Pfrp1d7BICqcAUFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIMKFB27tyZq666KhMmTMjw4cPz4Q9/ONdee236+voqa/r6+rJgwYKMGzcuw4cPT2tra/7yl7/0+z5btmzJrFmzUl9fn1GjRuXCCy/Ma6+9tmfOCAAY9AYUKD/84Q9z22235Sc/+UleeOGF/PCHP8yiRYty8803V9YsWrQoixcvzpIlS7JmzZqMGDEi06ZNy+uvv15ZM2vWrDz33HNZuXJlli9fnsceeyyzZ8/ec2cFAAxqNX3/ffnjXZx55plpbGzMnXfeWdk3Y8aMDB8+PPfcc0/6+vrS3Nyc73znO7nsssuSJF1dXWlsbMzSpUszc+bMvPDCC5k0aVLWrVuXKVOmJElWrFiRM844I//85z/T3Nz8rnN0d3enoaEhXV1dqa+vH+g5D2pHzvtltUdgH/r79dOrPQLAHjOQn98DuoJy4oknZtWqVfnzn/+cJHnmmWfyu9/9LqeffnqS5G9/+1s6OjrS2tpaeUxDQ0OmTp2a9vb2JEl7e3tGjRpViZMkaW1tTW1tbdasWbPL5+3p6Ul3d3e/DQDYfw0dyOJ58+alu7s7EydOzJAhQ7Jz5858//vfz6xZs5IkHR0dSZLGxsZ+j2tsbKwc6+joyNixY/sPMXRoRo8eXVnzVgsXLszVV189kFEBgEFsQFdQfv7zn+fee+/Nfffdl6eeeirLli3Lj370oyxbtmxvzZckmT9/frq6uirbxo0b9+rzAQDVNaArKJdffnnmzZuXmTNnJkmOOeaY/OMf/8jChQtz3nnnpampKUnS2dmZcePGVR7X2dmZY489NknS1NSUzZs39/u+b7zxRrZs2VJ5/FvV1dWlrq5uIKMCAIPYgK6g/Oc//0ltbf+HDBkyJL29vUmSCRMmpKmpKatWraoc7+7uzpo1a9LS0pIkaWlpydatW7N+/frKmkcffTS9vb2ZOnXqbp8IALD/GNAVlM9//vP5/ve/nyOOOCIf/ehH84c//CE33HBDvvrVryZJampqcskll+S6667LUUcdlQkTJuSqq65Kc3NzzjrrrCTJ0UcfndNOOy0XXXRRlixZkh07dmTOnDmZOXPme/oNHgBg/zegQLn55ptz1VVX5Rvf+EY2b96c5ubmfO1rX8uCBQsqa6644ops27Yts2fPztatW/PpT386K1asyLBhwypr7r333syZMyennHJKamtrM2PGjCxevHjPnRUAMKgN6D4opXAfFA4U7oMC7E/22n1QAAD2BYECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBhwo//rXv/KVr3wlY8aMyfDhw3PMMcfkySefrBzv6+vLggULMm7cuAwfPjytra35y1/+0u97bNmyJbNmzUp9fX1GjRqVCy+8MK+99tr7PxsAYL8woED597//nZNOOikHHXRQHnnkkTz//PP58Y9/nEMPPbSyZtGiRVm8eHGWLFmSNWvWZMSIEZk2bVpef/31yppZs2blueeey8qVK7N8+fI89thjmT179p47KwBgUKvp6+vre6+L582bl9///vf57W9/u8vjfX19aW5uzne+851cdtllSZKurq40NjZm6dKlmTlzZl544YVMmjQp69aty5QpU5IkK1asyBlnnJF//vOfaW5uftc5uru709DQkK6urtTX17/X8fcLR877ZbVHYB/6+/XTqz0CwB4zkJ/fA7qC8tBDD2XKlCn50pe+lLFjx+a4447LHXfcUTn+t7/9LR0dHWltba3sa2hoyNSpU9Pe3p4kaW9vz6hRoypxkiStra2pra3NmjVrdvm8PT096e7u7rcBAPuvAQXKX//619x222056qij8utf/zoXX3xxvvWtb2XZsmVJko6OjiRJY2Njv8c1NjZWjnV0dGTs2LH9jg8dOjSjR4+urHmrhQsXpqGhobKNHz9+IGMDAIPMgAKlt7c3xx9/fH7wgx/kuOOOy+zZs3PRRRdlyZIle2u+JMn8+fPT1dVV2TZu3LhXnw8AqK4BBcq4ceMyadKkfvuOPvrobNiwIUnS1NSUJOns7Oy3prOzs3Ksqakpmzdv7nf8jTfeyJYtWypr3qquri719fX9NgBg/zWgQDnppJPy0ksv9dv35z//OR/60IeSJBMmTEhTU1NWrVpVOd7d3Z01a9akpaUlSdLS0pKtW7dm/fr1lTWPPvpoent7M3Xq1N0+EQBg/zF0IIsvvfTSnHjiifnBD36Qc845J2vXrs3tt9+e22+/PUlSU1OTSy65JNddd12OOuqoTJgwIVdddVWam5tz1llnJfn/Ky6nnXZa5a2hHTt2ZM6cOZk5c+Z7+g0eAGD/N6BA+eQnP5kHH3ww8+fPzzXXXJMJEybkpptuyqxZsyprrrjiimzbti2zZ8/O1q1b8+lPfzorVqzIsGHDKmvuvffezJkzJ6ecckpqa2szY8aMLF68eM+dFQAwqA3oPiilcB8UDhTugwLsT/bafVAAAPYFgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHHeV6Bcf/31qampySWXXFLZ9/rrr6etrS1jxozJBz7wgcyYMSOdnZ39Hrdhw4ZMnz49hxxySMaOHZvLL788b7zxxvsZBQDYj+x2oKxbty4//elP8/GPf7zf/ksvvTQPP/xwHnjggaxevTqbNm3K2WefXTm+c+fOTJ8+Pdu3b8/jjz+eZcuWZenSpVmwYMHunwUAsF/ZrUB57bXXMmvWrNxxxx059NBDK/u7urpy55135oYbbsjnPve5TJ48OXfffXcef/zxPPHEE0mS3/zmN3n++edzzz335Nhjj83pp5+ea6+9Nrfccku2b9++Z84KABjUditQ2traMn369LS2tvbbv379+uzYsaPf/okTJ+aII45Ie3t7kqS9vT3HHHNMGhsbK2umTZuW7u7uPPfcc7t8vp6ennR3d/fbAID919CBPuD+++/PU089lXXr1r3tWEdHRw4++OCMGjWq3/7GxsZ0dHRU1vx3nLx5/M1ju7Jw4cJcffXVAx0VABikBnQFZePGjfn2t7+de++9N8OGDdtbM73N/Pnz09XVVdk2bty4z54bANj3BhQo69evz+bNm3P88cdn6NChGTp0aFavXp3Fixdn6NChaWxszPbt27N169Z+j+vs7ExTU1OSpKmp6W2/1fPm12+ueau6urrU19f32wCA/deAAuWUU07Js88+m6effrqyTZkyJbNmzar880EHHZRVq1ZVHvPSSy9lw4YNaWlpSZK0tLTk2WefzebNmytrVq5cmfr6+kyaNGkPnRYAMJgN6DMoI0eOzMc+9rF++0aMGJExY8ZU9l944YWZO3duRo8enfr6+nzzm99MS0tLTjjhhCTJqaeemkmTJuXcc8/NokWL0tHRkSuvvDJtbW2pq6vbQ6cFAAxmA/6Q7Lu58cYbU1tbmxkzZqSnpyfTpk3LrbfeWjk+ZMiQLF++PBdffHFaWloyYsSInHfeebnmmmv29CgAwCBV09fX11ftIQaqu7s7DQ0N6erqOuA+j3LkvF9WewT2ob9fP73aIwDsMQP5+e1v8QAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcfb4fVAA2D1uI3BgcRuBd+YKCgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFCcAQXKwoUL88lPfjIjR47M2LFjc9ZZZ+Wll17qt+b1119PW1tbxowZkw984AOZMWNGOjs7+63ZsGFDpk+fnkMOOSRjx47N5ZdfnjfeeOP9nw0AsF8YUKCsXr06bW1teeKJJ7Jy5crs2LEjp556arZt21ZZc+mll+bhhx/OAw88kNWrV2fTpk05++yzK8d37tyZ6dOnZ/v27Xn88cezbNmyLF26NAsWLNhzZwUADGo1fX19fbv74Jdffjljx47N6tWrc/LJJ6erqyuHH3547rvvvnzxi19Mkrz44os5+uij097enhNOOCGPPPJIzjzzzGzatCmNjY1JkiVLluS73/1uXn755Rx88MHv+rzd3d1paGhIV1dX6uvrd3f8QenIeb+s9gjsQ3+/fnq1R2Af8vo+sByIr++B/Px+X59B6erqSpKMHj06SbJ+/frs2LEjra2tlTUTJ07MEUcckfb29iRJe3t7jjnmmEqcJMm0adPS3d2d5557bpfP09PTk+7u7n4bALD/2u1A6e3tzSWXXJKTTjopH/vYx5IkHR0dOfjggzNq1Kh+axsbG9PR0VFZ899x8ubxN4/tysKFC9PQ0FDZxo8fv7tjAwCDwG4HSltbW/70pz/l/vvv35Pz7NL8+fPT1dVV2TZu3LjXnxMAqJ6hu/OgOXPmZPny5XnsscfywQ9+sLK/qakp27dvz9atW/tdRens7ExTU1Nlzdq1a/t9vzd/y+fNNW9VV1eXurq63RkVABiEBnQFpa+vL3PmzMmDDz6YRx99NBMmTOh3fPLkyTnooIOyatWqyr6XXnopGzZsSEtLS5KkpaUlzz77bDZv3lxZs3LlytTX12fSpEnv51wAgP3EgK6gtLW15b777ssvfvGLjBw5svKZkYaGhgwfPjwNDQ258MILM3fu3IwePTr19fX55je/mZaWlpxwwglJklNPPTWTJk3Kueeem0WLFqWjoyNXXnll2traXCUBAJIMMFBuu+22JMlnP/vZfvvvvvvunH/++UmSG2+8MbW1tZkxY0Z6enoybdq03HrrrZW1Q4YMyfLly3PxxRenpaUlI0aMyHnnnZdrrrnm/Z0JALDfGFCgvJdbpgwbNiy33HJLbrnllv+55kMf+lB+9atfDeSpAYADiL/FAwAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFCcqgbKLbfckiOPPDLDhg3L1KlTs3bt2mqOAwAUomqB8rOf/Sxz587N9773vTz11FP5xCc+kWnTpmXz5s3VGgkAKETVAuWGG27IRRddlAsuuCCTJk3KkiVLcsghh+Suu+6q1kgAQCGGVuNJt2/fnvXr12f+/PmVfbW1tWltbU17e/vb1vf09KSnp6fydVdXV5Kku7t77w9bmN6e/1R7BPahA/Hf8QOZ1/eB5UB8fb95zn19fe+6tiqB8sorr2Tnzp1pbGzst7+xsTEvvvji29YvXLgwV1999dv2jx8/fq/NCCVouKnaEwB7y4H8+n711VfT0NDwjmuqEigDNX/+/MydO7fydW9vb7Zs2ZIxY8akpqamipOxL3R3d2f8+PHZuHFj6uvrqz0OsAd5fR9Y+vr68uqrr6a5ufld11YlUA477LAMGTIknZ2d/fZ3dnamqanpbevr6upSV1fXb9+oUaP25ogUqL6+3n/AYD/l9X3geLcrJ2+qyodkDz744EyePDmrVq2q7Ovt7c2qVavS0tJSjZEAgIJU7S2euXPn5rzzzsuUKVPyqU99KjfddFO2bduWCy64oFojAQCFqFqgfPnLX87LL7+cBQsWpKOjI8cee2xWrFjxtg/OQl1dXb73ve+97W0+YPDz+uZ/qel7L7/rAwCwD/lbPABAcQQKAFAcgQIAFEegAADFESgAQHEGxa3uObC88sorueuuu9Le3p6Ojo4kSVNTU0488cScf/75Ofzww6s8IQB7mysoFGXdunX5yEc+ksWLF6ehoSEnn3xyTj755DQ0NGTx4sWZOHFinnzyyWqPCewlGzduzFe/+tVqj0EB3AeFopxwwgn5xCc+kSVLlrztD0H29fXl61//ev74xz+mvb29ShMCe9MzzzyT448/Pjt37qz2KFSZt3goyjPPPJOlS5fu8q9U19TU5NJLL81xxx1XhcmAPeGhhx56x+N//etf99EklE6gUJSmpqasXbs2EydO3OXxtWvX+nMIMIidddZZqampyTtdvN/V/6Bw4BEoFOWyyy7L7Nmzs379+pxyyimVGOns7MyqVatyxx135Ec/+lGVpwR217hx43LrrbfmC1/4wi6PP/3005k8efI+nooSCRSK0tbWlsMOOyw33nhjbr311sr70EOGDMnkyZOzdOnSnHPOOVWeEthdkydPzvr16/9noLzb1RUOHD4kS7F27NiRV155JUly2GGH5aCDDqryRMD79dvf/jbbtm3Laaedtsvj27Zty5NPPpnPfOYz+3gySiNQAIDiuA8KAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBx/g8nDOwFBHFrLwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ips'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Ips', ylabel='Price'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Ips'], y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x', n=1, expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['x_res'] = new[0]\n",
    "df['y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['x_res'] = df['x_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['x_res'] = df['x_res'].astype('Int32')\n",
    "df['y_res'] = df['x_res'].astype('Int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float32\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float32\n",
      " 11  Touchscreen       1303 non-null   int64  \n",
      " 12  Ips               1303 non-null   int64  \n",
      " 13  x_res             1303 non-null   Int32  \n",
      " 14  y_res             1303 non-null   Int32  \n",
      "dtypes: Int32(2), float32(3), int32(1), int64(2), object(7)\n",
      "memory usage: 124.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\2894258535.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['Price']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "x_res          0.556529\n",
       "y_res          0.556529\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['x_res']**2) + (df['y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\815546952.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['Price']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "x_res          0.556529\n",
       "y_res          0.556529\n",
       "ppi            0.474973\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution','Inches','x_res','y_res'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         27\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['OpSys'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.679688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.523438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153.117857</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.343750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.476309</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.804688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight          Price  Touchscreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.679688            0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.523438            0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.000000            0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.343750            0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.804688            0   \n",
       "\n",
       "   Ips         ppi       Cpu Name  \n",
       "0    1  272.209524  Intel Core i5  \n",
       "1    0  153.117857  Intel Core i5  \n",
       "2    0  174.057050  Intel Core i5  \n",
       "3    1  264.476309  Intel Core i7  \n",
       "4    1  272.209524  Intel Core i5  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Intel Core i5', 'Intel Core i7', 'AMD A9-Series 9420',\n",
       "       'Intel Core i3', 'Intel Core M', 'Intel Atom x5-Z8350',\n",
       "       'AMD E-Series E2-9000e', 'Intel Atom x5-Z8300',\n",
       "       'AMD E-Series E2-6110', 'AMD A6-Series 9220', 'Intel Celeron Dual',\n",
       "       'AMD Ryzen 1700', 'Intel Pentium Quad', 'Intel Atom x5-Z8550',\n",
       "       'AMD FX 9830P', 'AMD E-Series 6110', 'Intel Xeon E3-1505M',\n",
       "       'AMD E-Series 9000e', 'AMD A10-Series A10-9620P',\n",
       "       'AMD A6-Series A6-9220', 'AMD A10-Series 9600P',\n",
       "       'AMD A8-Series 7410', 'AMD A12-Series 9720P', 'Intel Celeron Quad',\n",
       "       'AMD Ryzen 1600', 'AMD A10-Series 9620P', 'AMD E-Series 7110',\n",
       "       'AMD A9-Series A9-9420', 'Intel Xeon E3-1535M',\n",
       "       'AMD E-Series E2-9000', 'AMD A6-Series 7310', 'Intel Atom Z8350',\n",
       "       'Intel Pentium Dual', 'AMD A12-Series 9700P', 'AMD A4-Series 7210',\n",
       "       'AMD FX 8800P', 'Intel Atom X5-Z8350', 'Samsung Cortex A72&A53',\n",
       "       'AMD E-Series 9000', 'AMD A9-Series 9410'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cpu_config(cpu):\n",
    "    if (cpu == 'Intel Core i7' or cpu == 'Intel Core i5' or cpu == 'Intel Core i3'):\n",
    "        return cpu\n",
    "    else:\n",
    "        if cpu.split()[0] == 'Intel':\n",
    "            return (\"Another Intel Processar\")\n",
    "        else:\n",
    "            return ('AMD Processar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu BrandName'] = df['Cpu Name'].apply(Cpu_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Cpu BrandName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Cpu BrandName'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu Name', 'Cpu'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company        1303 non-null   object \n",
      " 1   TypeName       1303 non-null   object \n",
      " 2   Ram            1303 non-null   int32  \n",
      " 3   Memory         1303 non-null   object \n",
      " 4   Gpu            1303 non-null   object \n",
      " 5   OpSys          1303 non-null   object \n",
      " 6   Weight         1303 non-null   float32\n",
      " 7   Price          1303 non-null   float32\n",
      " 8   Touchscreen    1303 non-null   int64  \n",
      " 9   Ips            1303 non-null   int64  \n",
      " 10  ppi            1303 non-null   float64\n",
      " 11  Cpu BrandName  1303 non-null   object \n",
      "dtypes: float32(2), float64(1), int32(1), int64(2), object(6)\n",
      "memory usage: 107.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256GB SSD                        412\n",
       "1TB HDD                          223\n",
       "500GB HDD                        132\n",
       "512GB SSD                        118\n",
       "128GB SSD +  1TB HDD              94\n",
       "128GB SSD                         76\n",
       "256GB SSD +  1TB HDD              73\n",
       "32GB Flash Storage                38\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "512GB SSD +  1TB HDD              14\n",
       "1TB SSD                           14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB SSD +  256GB SSD             1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "32GB HDD                           1\n",
       "64GB SSD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: Memory, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Memory.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Memory.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\4023190604.py:16: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['first'] = df['first'].str.replace(r'\\D', '')\n",
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\4023190604.py:25: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['second'] = df['second'].str.replace(r'\\D', '')\n"
     ]
    }
   ],
   "source": [
    "df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('GB', '')\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
    "new = df[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
    "\n",
    "df[\"first\"]= new[0]\n",
    "df[\"first\"]=df[\"first\"].str.strip()\n",
    "\n",
    "df[\"second\"]= new[1]\n",
    "\n",
    "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['first'] = df['first'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"second\"].fillna(\"0\", inplace = True)\n",
    "\n",
    "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['second'] = df['second'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"first\"] = df[\"first\"].astype(int)\n",
    "df[\"second\"] = df[\"second\"].astype(int)\n",
    "\n",
    "df[\"HDD\"]=(df[\"first\"]*df[\"Layer1HDD\"]+df[\"second\"]*df[\"Layer2HDD\"])\n",
    "df[\"SSD\"]=(df[\"first\"]*df[\"Layer1SSD\"]+df[\"second\"]*df[\"Layer2SSD\"])\n",
    "df[\"Hybrid\"]=(df[\"first\"]*df[\"Layer1Hybrid\"]+df[\"second\"]*df[\"Layer2Hybrid\"])\n",
    "df[\"Flash_Storage\"]=(df[\"first\"]*df[\"Layer1Flash_Storage\"]+df[\"second\"]*df[\"Layer2Flash_Storage\"])\n",
    "\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
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       "      <th>Touchscreen</th>\n",
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       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
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       "      <td>128 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.679688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
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       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.523438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153.117857</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.343750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.476309</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.804688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram             Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128 SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128 Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256 SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512 SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256 SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight          Price  Touchscreen  Ips         ppi  Cpu BrandName  \\\n",
       "0  macOS    1.37   71378.679688            0    1  272.209524  Intel Core i5   \n",
       "1  macOS    1.34   47895.523438            0    0  153.117857  Intel Core i5   \n",
       "2  No OS    1.86   30636.000000            0    0  174.057050  Intel Core i5   \n",
       "3  macOS    1.83  135195.343750            0    1  264.476309  Intel Core i7   \n",
       "4  macOS    1.37   96095.804688            0    1  272.209524  Intel Core i5   \n",
       "\n",
       "   HDD  SSD  Hybrid  Flash_Storage  \n",
       "0    0  128       0              0  \n",
       "1    0    0       0            128  \n",
       "2    0  256       0              0  \n",
       "3    0  512       0              0  \n",
       "4    0  256       0              0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\1734695575.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()[\"Price\"]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            1.000000\n",
       "Touchscreen      0.191226\n",
       "Ips              0.252208\n",
       "ppi              0.474973\n",
       "HDD             -0.096441\n",
       "SSD              0.670799\n",
       "Hybrid           0.007989\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid', 'Flash_Storage'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.34</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.343750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.476309</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.804688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "           Price  Touchscreen  Ips         ppi  Cpu BrandName  HDD  SSD  \n",
       "0   71378.679688            0    1  272.209524  Intel Core i5    0  128  \n",
       "1   47895.523438            0    0  153.117857  Intel Core i5    0    0  \n",
       "2   30636.000000            0    0  174.057050  Intel Core i5    0  256  \n",
       "3  135195.343750            0    1  264.476309  Intel Core i7    0  512  \n",
       "4   96095.804688            0    1  272.209524  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel HD Graphics 620      281\n",
       "Intel HD Graphics 520      185\n",
       "Intel UHD Graphics 620      68\n",
       "Nvidia GeForce GTX 1050     66\n",
       "Nvidia GeForce GTX 1060     48\n",
       "                          ... \n",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: Gpu, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df['Gpu'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu Brand'] = df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['Gpu'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.343750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.476309</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.804688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight          Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.679688            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.523438            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.000000            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.343750            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.804688            0    1   \n",
       "\n",
       "          ppi  Cpu BrandName  HDD  SSD Gpu Brand  \n",
       "0  272.209524  Intel Core i5    0  128     Intel  \n",
       "1  153.117857  Intel Core i5    0    0     Intel  \n",
       "2  174.057050  Intel Core i5    0  256     Intel  \n",
       "3  264.476309  Intel Core i7    0  512       AMD  \n",
       "4  272.209524  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: Gpu Brand, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu Brand'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gpu Brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: Gpu Brand, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu Brand'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Gpu Brand'],y=df['Price'],estimator=np.median)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         26\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['OpSys'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def os_name(inp):\n",
    "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others/No OS/Linux'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Os'] = df['OpSys'].apply(os_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu BrandName</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>Os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.679688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.523438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153.117857</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.343750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.476309</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.804688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight          Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.679688            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.523438            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.000000            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.343750            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.804688            0    1   \n",
       "\n",
       "          ppi  Cpu BrandName  HDD  SSD Gpu Brand                  Os  \n",
       "0  272.209524  Intel Core i5    0  128     Intel                 Mac  \n",
       "1  153.117857  Intel Core i5    0    0     Intel                 Mac  \n",
       "2  174.057050  Intel Core i5    0  256     Intel  Others/No OS/Linux  \n",
       "3  264.476309  Intel Core i7    0  512       AMD                 Mac  \n",
       "4  272.209524  Intel Core i5    0  256     Intel                 Mac  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['OpSys'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Os'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\815546952.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['Price']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ram            0.742905\n",
       "Weight         0.209867\n",
       "Price          1.000000\n",
       "Touchscreen    0.192917\n",
       "Ips            0.253320\n",
       "ppi            0.476578\n",
       "HDD           -0.096891\n",
       "SSD            0.670660\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\58359773.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Local\\Temp\\ipykernel_40804\\3556049916.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(np.log(df['Price']))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(df['Price']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL BULDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, df.columns != 'Price']\n",
    "y = np.log(df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu BrandName</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>Os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153.117857</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.476309</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>272.209524</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>193.949289</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>16</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>340.261905</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137.986838</td>\n",
       "      <td>Another Intel Processar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.834338</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.834338</td>\n",
       "      <td>Another Intel Processar</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "0      Apple           Ultrabook    8    1.37            0    1  272.209524   \n",
       "1      Apple           Ultrabook    8    1.34            0    0  153.117857   \n",
       "2         HP            Notebook    8    1.86            0    0  174.057050   \n",
       "3      Apple           Ultrabook   16    1.83            0    1  264.476309   \n",
       "4      Apple           Ultrabook    8    1.37            0    1  272.209524   \n",
       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  193.949289   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  340.261905   \n",
       "1300  Lenovo            Notebook    2    1.50            0    0  137.986838   \n",
       "1301      HP            Notebook    6    2.19            0    0  123.834338   \n",
       "1302    Asus            Notebook    4    2.20            0    0  123.834338   \n",
       "\n",
       "                Cpu BrandName   HDD  SSD Gpu Brand                  Os  \n",
       "0               Intel Core i5     0  128     Intel                 Mac  \n",
       "1               Intel Core i5     0    0     Intel                 Mac  \n",
       "2               Intel Core i5     0  256     Intel  Others/No OS/Linux  \n",
       "3               Intel Core i7     0  512       AMD                 Mac  \n",
       "4               Intel Core i5     0  256     Intel                 Mac  \n",
       "...                       ...   ...  ...       ...                 ...  \n",
       "1298            Intel Core i7     0  128     Intel             Windows  \n",
       "1299            Intel Core i7     0  512     Intel             Windows  \n",
       "1300  Another Intel Processar     0    0     Intel             Windows  \n",
       "1301            Intel Core i7  1000    0       AMD             Windows  \n",
       "1302  Another Intel Processar   500    0     Intel             Windows  \n",
       "\n",
       "[1302 rows x 12 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11.175755\n",
       "1       10.776777\n",
       "2       10.329931\n",
       "3       11.814476\n",
       "4       11.473101\n",
       "          ...    \n",
       "1298    10.433899\n",
       "1299    11.288115\n",
       "1300     9.409283\n",
       "1301    10.614129\n",
       "1302     9.886357\n",
       "Name: Price, Length: 1302, dtype: float32"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu BrandName</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>Os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.834338</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.535833</td>\n",
       "      <td>Another Intel Processar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.834338</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.057050</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.834338</td>\n",
       "      <td>AMD Processar</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.834338</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>271.529004</td>\n",
       "      <td>Another Intel Processar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1106 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "183   Toshiba            Notebook    8    2.00            0    0  123.834338   \n",
       "1141      MSI              Gaming    8    2.40            0    0  174.057050   \n",
       "1049     Asus             Netbook    4    1.20            0    0  166.535833   \n",
       "1020     Dell  2 in 1 Convertible    4    2.08            1    1  174.057050   \n",
       "878      Dell            Notebook    4    2.18            0    0  174.057050   \n",
       "...       ...                 ...  ...     ...          ...  ...         ...   \n",
       "466      Acer            Notebook    4    2.20            0    0  123.834338   \n",
       "299      Asus           Ultrabook   16    1.63            0    0  174.057050   \n",
       "493      Acer            Notebook    8    2.20            0    0  123.834338   \n",
       "527    Lenovo            Notebook    8    2.20            0    0  123.834338   \n",
       "1193    Apple           Ultrabook    8    0.92            0    1  271.529004   \n",
       "\n",
       "                Cpu BrandName   HDD  SSD Gpu Brand                  Os  \n",
       "183             Intel Core i5     0  128     Intel             Windows  \n",
       "1141            Intel Core i7  1000  128    Nvidia             Windows  \n",
       "1049  Another Intel Processar     0    0     Intel  Others/No OS/Linux  \n",
       "1020            Intel Core i3  1000    0     Intel             Windows  \n",
       "878             Intel Core i5  1000  128    Nvidia             Windows  \n",
       "...                       ...   ...  ...       ...                 ...  \n",
       "466             Intel Core i3   500    0    Nvidia             Windows  \n",
       "299             Intel Core i7     0  512    Nvidia             Windows  \n",
       "493             AMD Processar  1000    0       AMD             Windows  \n",
       "527             Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
       "1193  Another Intel Processar     0    0     Intel                 Mac  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183     10.651384\n",
       "1141    11.016798\n",
       "1049     9.638174\n",
       "1020    10.655148\n",
       "878     10.791749\n",
       "          ...    \n",
       "466     10.126163\n",
       "299     11.267218\n",
       "493     10.336864\n",
       "527     10.104611\n",
       "1193    11.034319\n",
       "Name: Price, Length: 1106, dtype: float32"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu BrandName</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>Os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.834338</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "183  Toshiba  Notebook    8     2.0            0    0  123.834338   \n",
       "\n",
       "     Cpu BrandName  HDD  SSD Gpu Brand       Os  \n",
       "183  Intel Core i5    0  128     Intel  Windows  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LINEAR REAGRESSION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step1 = ColumnTransformer(transformers=[\n",
    "#     ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "# ],remainder='passthrough')\n",
    "\n",
    "# step2 = LinearRegression()\n",
    "\n",
    "# pipe = Pipeline([\n",
    "#     ('step1',step1),\n",
    "#     ('step2',step2)\n",
    "# ])\n",
    "\n",
    "# pipe.fit(X_train,y_train)\n",
    "\n",
    "# y_pred = pipe.predict(X_test)\n",
    "\n",
    "# print('R2 score',r2_score(y_test,y_pred))\n",
    "# print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# # Define the parameter grid for Ridge regression\n",
    "# param_grid = {\n",
    "#     'step2__alpha': [0.1, 1.0, 10.0]\n",
    "# }\n",
    "\n",
    "# # Step 1: Column transformation\n",
    "# step1 = ColumnTransformer(transformers=[\n",
    "#     ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0, 1, 7, 10, 11])\n",
    "# ], remainder='passthrough')\n",
    "\n",
    "# # Step 2: Ridge Regression\n",
    "# step2 = Ridge()\n",
    "\n",
    "# # Create the pipeline\n",
    "# pipe = Pipeline([\n",
    "#     ('step1', step1),\n",
    "#     ('step2', step2)\n",
    "# ])\n",
    "\n",
    "# # Create GridSearchCV with the pipeline and parameter grid\n",
    "# grid_search = GridSearchCV(pipe, param_grid, cv=5, scoring='neg_mean_absolute_error')\n",
    "\n",
    "# # Fit the GridSearchCV to your data\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "# # Get the best estimator from the grid search\n",
    "# best_pipe = grid_search.best_estimator_\n",
    "\n",
    "# # Make predictions using the best estimator\n",
    "# y_pred = best_pipe.predict(X_test)\n",
    "\n",
    "# print('Best parameters:', grid_search.best_params_)\n",
    "# print('Best negative MAE:', grid_search.best_score_)\n",
    "# print('R2 score:', r2_score(y_test, y_pred))\n",
    "# print('MAE:', mean_absolute_error(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {\n",
    "#     'step2__n_estimators': [100, 300, 400, 450],\n",
    "#     'step2__learning_rate': [0.01, 0.07, 0.09 ,0.1],\n",
    "#     'step2__max_depth': [3, 4, 5, 7]\n",
    "# }\n",
    "\n",
    "# # Step 1: Column transformation\n",
    "# step1 = ColumnTransformer(transformers=[\n",
    "#     ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0, 1, 7, 10, 11])\n",
    "# ], remainder='passthrough')\n",
    "\n",
    "# # Step 2: XGBoost Regressor\n",
    "# step2 = XGBRegressor()\n",
    "\n",
    "# # Create the pipeline\n",
    "# pipe = Pipeline([\n",
    "#     ('step1', step1),\n",
    "#     ('step2', step2)\n",
    "# ])\n",
    "\n",
    "# # Create GridSearchCV with the pipeline and parameter grid\n",
    "# grid_search = GridSearchCV(pipe, param_grid, cv=5, scoring='neg_mean_absolute_error')\n",
    "\n",
    "# # Fit the GridSearchCV to your data\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "# # Get the best estimator from the grid search\n",
    "# best_pipe = grid_search.best_estimator_\n",
    "\n",
    "# # Make predictions using the best estimator\n",
    "# y_pred = best_pipe.predict(X_test)\n",
    "\n",
    "# print('Best parameters:', grid_search.best_params_)\n",
    "# print('R2 score:', r2_score(y_test, y_pred))\n",
    "# print('MAE:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {\n",
    "#     'step2__n_estimators': [100, 200, 300],\n",
    "#     'step2__max_features': [0.75, 0.8, 0.9],\n",
    "#     'step2__max_depth': [15, 17, 20]  # You can also try other values\n",
    "# }\n",
    "\n",
    "# # Step 1: Column transformation\n",
    "# step1 = ColumnTransformer(transformers=[\n",
    "#     ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0, 1, 7, 10, 11])\n",
    "# ], remainder='passthrough')\n",
    "\n",
    "# # Step 2: Random Forest Regressor\n",
    "# step2 = RandomForestRegressor()\n",
    "\n",
    "# # Create the pipeline\n",
    "# pipe = Pipeline([\n",
    "#     ('step1', step1),\n",
    "#     ('step2', step2)\n",
    "# ])\n",
    "\n",
    "# # Create GridSearchCV with the pipeline and parameter grid\n",
    "# grid_search = GridSearchCV(pipe, param_grid, cv=5, scoring='neg_mean_absolute_error')\n",
    "\n",
    "# # Fit the GridSearchCV to your data\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "# # Get the best estimator from the grid search\n",
    "# best_pipe = grid_search.best_estimator_\n",
    "\n",
    "# # Make predictions using the best estimator\n",
    "# y_pred = best_pipe.predict(X_test)\n",
    "\n",
    "# print('Best parameters:', grid_search.best_params_)\n",
    "# print('Best negative MAE:', grid_search.best_score_)\n",
    "# print('R2 score:', r2_score(y_test, y_pred))\n",
    "# print('MAE:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiv\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8907967559859193\n",
      "MAE 0.15575334528551382\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
    "\n",
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0, 1, 7, 10, 11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=200, random_state=3, max_features=0.75, max_depth=17)\n",
    "gbdt = GradientBoostingRegressor(n_estimators=100, max_features=0.5)\n",
    "# xgb = XGBRegressor(n_estimators=25, learning_rate=0.3, max_depth=5)\n",
    "et = ExtraTreesRegressor(n_estimators=100, random_state=3, max_depth=10)\n",
    "\n",
    "step2 = VotingRegressor([('rf', rf), ('gbdt', gbdt), ('et', et)], weights=[5, 1, 1])\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score', r2_score(y_test, y_pred))\n",
    "print('MAE', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  }
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