Laptop Price Predictor.ipynb

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   "source": [
    "# Laptop Price Predictor - Team 8"
   ]
  },
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   "source": [
    "# Importing the Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "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\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
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   "source": [
    "# lodaing the dataset\n",
    "df = pd.read_csv('laptop_data.csv')"
   ]
  },
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>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",
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      "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  "
      ]
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   "source": [
    "# Lets look at the dataset\n",
    "df.head()"
   ]
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      "text/plain": [
       "(1303, 12)"
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   "source": [
    "# Checking the shape of the dataset\n",
    "df.shape"
   ]
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     "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": [
    "# More info on the dataset\n",
    "df.info()"
   ]
  },
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   "source": [
    "# Checking for duplicate values of data\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46850ae8",
   "metadata": {},
   "source": [
    "# Data Preprocessing and Visualization"
   ]
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   "execution_count": 7,
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   "source": [
    "df=df.drop(\"Unnamed: 0\", axis=1)"
   ]
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    {
     "data": {
      "text/plain": [
       "Company             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Memory              0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
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   "source": [
    "# Checking null values in the dataset\n",
    "df.isnull().sum()"
   ]
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   "source": [
    "df['Ram'] = df['Ram'].str.replace('GB','')\n",
    "df['Weight'] = df['Weight'].str.replace('kg','')\n",
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
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   "cell_type": "markdown",
   "id": "32ae4a71",
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   "source": [
    "Checking the `Price` attribute, we can see that it is somehow left-skewed, with majority of the laptops bein in the range of `50,000-150,000`"
   ]
  },
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiva\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
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    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e91984",
   "metadata": {},
   "source": [
    "Lets have a look at the different laptop brands - we can see that `Dell` dominates the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7059245b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:56.691247Z",
     "iopub.status.busy": "2022-11-16T06:00:56.690259Z",
     "iopub.status.idle": "2022-11-16T06:00:56.999575Z",
     "shell.execute_reply": "2022-11-16T06:00:56.998427Z"
    },
    "papermill": {
     "duration": 0.342004,
     "end_time": "2022-11-16T06:00:57.002505",
     "exception": false,
     "start_time": "2022-11-16T06:00:56.660501",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecc2e0c",
   "metadata": {},
   "source": [
    "How does the brand relate to pricing of a laptop ? Of course, the more extablised brands will sell their latops at a higher price than their competitors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d4d7ea9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:57.057778Z",
     "iopub.status.busy": "2022-11-16T06:00:57.057326Z",
     "iopub.status.idle": "2022-11-16T06:00:57.779716Z",
     "shell.execute_reply": "2022-11-16T06:00:57.778283Z"
    },
    "papermill": {
     "duration": 0.753039,
     "end_time": "2022-11-16T06:00:57.782475",
     "exception": false,
     "start_time": "2022-11-16T06:00:57.029436",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Company'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd076d98",
   "metadata": {},
   "source": [
    "What different kinds of laptops we are dealing with?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a77d064e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:57.837304Z",
     "iopub.status.busy": "2022-11-16T06:00:57.836920Z",
     "iopub.status.idle": "2022-11-16T06:00:58.041488Z",
     "shell.execute_reply": "2022-11-16T06:00:58.040562Z"
    },
    "papermill": {
     "duration": 0.234766,
     "end_time": "2022-11-16T06:00:58.043798",
     "exception": false,
     "start_time": "2022-11-16T06:00:57.809032",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5614d548",
   "metadata": {},
   "source": [
    "How is a specific type of laptop related to price? Is a `Gaming` laptop more expensive? What about others? Turns out that `Workstation` is the most expensive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c971af2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:58.099542Z",
     "iopub.status.busy": "2022-11-16T06:00:58.098846Z",
     "iopub.status.idle": "2022-11-16T06:00:58.461889Z",
     "shell.execute_reply": "2022-11-16T06:00:58.460737Z"
    },
    "papermill": {
     "duration": 0.393883,
     "end_time": "2022-11-16T06:00:58.464574",
     "exception": false,
     "start_time": "2022-11-16T06:00:58.070691",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z1coAh4+wv4aJeYT4SMcMPbE0V9JCSQuXLVs2ctYREfGUNFFglgBLbF9evp9DVXB+LenZAOX9nq79t+86fhpwV4lPGyY+5BhJE4HNgOW9idg+0fYM2zOmTJnSh0uLiIiOgRcY20uBOyS9sIT2Bm4AFgCzS2w2cG75vACYVUaG7UjVmX9FaUZbKWnPMnrskJ5jOuc6ELgo/S8REYM18E7+4jDgG5I2AG4B3kNV7M6WNAe4HTgIwPb1ks6mKkKrgENtry7n+QBwKrAxVed+54mbJwGnlwEBy6lGoUVExAA1UmBs/4JqCZpee69h/6OBo4eJLwR2Hyb+MKVARUREM5rog4mIiHEgBSYiImqRAhMREbVIgYmIiFqkwERERC1SYCIiohYpMBERUYsUmIiIqEUKTERE1CIFJiIiapECExERtUiBiYiIWqTARERELVJgIiKiFikwERFRi6YeOBYxZs2bN4+lS5ey3XbbceyxxzadTsSolQIT8RQtXbqUO++8s+k0Ika9NJFFREQtUmAiIqIWaSKLvksfRURACkzUIH0UEQFpIouIiJo0VmAkTZD0c0nfL9+3lHSBpJvL+xZd+x4habGkmyTt2xXfQ9Kisu0LklTiG0o6q8QvlzR94BcYETHONdlE9iHgRmBy+X44cKHtYyQdXr7/raRdgVnAbsBzgP+U9ALbq4ETgLnAz4AfAPsB5wFzgPtt7yRpFvAp4B2Du7Sx4/ZP/E7fz7lq+ZbARFYt/1Xfz7/DPyzq6/kioj6N3MFImgb8AfC1rvD+wPzyeT5wQFf8TNuP2L4VWAzMlPRsYLLty2wbOK3nmM65zgH27tzdRETEYDTVRPY5YB7wWFdsW9t3A5T3bUp8KnBH135LSmxq+dwbH3KM7VXACmCrvl5BRESMaOAFRtJbgHtsX7WuhwwT8wjxkY7pzWWupIWSFi5btmwd04mIiHXRxB3MXsDbJN0GnAn8nqSvA78uzV6U93vK/kuA7buOnwbcVeLThokPOUbSRGAzYHlvIrZPtD3D9owpU6b05+qCrTd6jG03XsXWGz229p0jorUGXmBsH2F7mu3pVJ33F9k+GFgAzC67zQbOLZ8XALPKyLAdgZ2BK0oz2kpJe5b+lUN6jumc68DyM550BxP1+OiLH+CYmcv56IsfaDqViGjQaJpoeQxwtqQ5wO3AQQC2r5d0NnADsAo4tIwgA/gAcCqwMdXosfNK/CTgdEmLqe5cZg3qIiIiotJogbF9CXBJ+XwfsPca9jsaOHqY+EJg92HiD1MKVERENCMz+SMiohYpMBERUYsUmIiIqEUKTERE1GI0jSKL6Lu9vrhX38+5wQMbsB7rcccDd/T9/D857Cd9PV9Ek3IHExERtUiBiYiIWqTARERELVJgIiKiFikwERFRixSYiIioRQpMRETUIgUmIiJqkQITERG1SIGJiIhapMBEREQtUmAiIqIWKTAREVGLFJiIiKhFCkxERNQiBSYiImox8AIjaXtJF0u6UdL1kj5U4ltKukDSzeV9i65jjpC0WNJNkvbtiu8haVHZ9gVJKvENJZ1V4pdLmj7o64yIGO+auINZBfy17RcBewKHStoVOBy40PbOwIXlO2XbLGA3YD/geEkTyrlOAOYCO5fXfiU+B7jf9k7AccCnBnFhERHxhIEXGNt32766fF4J3AhMBfYH5pfd5gMHlM/7A2fafsT2rcBiYKakZwOTbV9m28BpPcd0znUOsHfn7ibimfIm5rFJj+FN3HQqEaPaxCZ/eGm6ehlwObCt7buhKkKStim7TQV+1nXYkhJ7tHzujXeOuaOca5WkFcBWwL31XEmMJ4/u9WjTKUSMCY118kt6FvAt4K9sPzjSrsPEPEJ8pGN6c5graaGkhcuWLVtbyhER8RQ0UmAkrU9VXL5h+9sl/OvS7EV5v6fElwDbdx0+DbirxKcNEx9yjKSJwGbA8t48bJ9oe4btGVOmTOnHpUVERNHEKDIBJwE32v5s16YFwOzyeTZwbld8VhkZtiNVZ/4VpTltpaQ9yzkP6Tmmc64DgYtKP01ERAxIE30wewHvBhZJ+kWJ/R1wDHC2pDnA7cBBALavl3Q2cAPVCLRDba8ux30AOBXYGDivvKAqYKdLWkx15zKr5muKiIgeAy8wtn/M8H0kAHuv4ZijgaOHiS8Edh8m/jClQEVERDMykz8iImqRAhMREbVIgYmIiFqkwERERC1SYCIiohYpMBERUYsUmIiIqEUKTERE1CIFJiIiapECExERtUiBiYiIWqTARERELVJgIiKiFikwERFRixSYiIioRQpMRETUIgUmIiJqkQITERG1SIGJiIhapMBEREQtUmAiIqIWKTAREVGLVhcYSftJuknSYkmHN51PRMR40toCI2kC8K/Am4BdgXdK2rXZrCIixo/WFhhgJrDY9i22fwucCezfcE4REeNGmwvMVOCOru9LSiwiIgZAtpvOoRaSDgL2tf1n5fu7gZm2D+vaZy4wt3x9IXDTAFPcGrh3gD9v0HJ9Y1uub+wa9LU91/aU4TZMHGASg7YE2L7r+zTgru4dbJ8InDjIpDokLbQ9o4mfPQi5vrEt1zd2jaZra3MT2ZXAzpJ2lLQBMAtY0HBOERHjRmvvYGyvkvSXwA+BCcDJtq9vOK2IiHGjtQUGwPYPgB80nccaNNI0N0C5vrEt1zd2jZpra20nf0RENKvNfTAREdGgFJiIiKhFCswASdpwmNiWTeQSMd5I2ljSC5vOYzxJgRmsb0tav/NF0rOBCxrMp6+GK5aSdmwilzq0+fokvXyY1/MltWIgkKS3Ar8A/qN8f6mkTFuoWSv+8Ywh3wW+KentVJNAFwAfbTSj/vqepDfZfhCgLC56NrB7s2n1TZuv73jg5cC1gKiu6VpgK0nvt31+k8n1wVFU6xNeAmD7F5KmN5hPX0maArwPmE7X73Xb720qJ0iBGSjbXy2TPr9L9Q/hz23/tNGk+uufqH4J/wHV0junAe9qNqW+avP13QbM6cwVK8Xzb4BPAt8GxnqBWWV7haSm86jLucB/Af8JrG44l8elwAyApI90f6W6e/kFsKekPW1/tpHE+sz2v5cmwPOBTYEDbN/ccFp90/Lr26V7IrLtGyS9zPYtLfmlfJ2kPwEmSNoZ+CDQpj/uNrH9t00n0SsFZjA27fn+nTXExyRJXwS6J1RNBm4BDpOE7Q82k1l/tP36ipsknUD1WAuAdwD/XQamPNpcWn1zGPAx4BHgDKoVPj7ZaEb99X1Jby6Ty0eNTLRsgKRNAdv+TdO59IOk2SNttz1/ULnUoe3XB9UIK+AvgNdQ3WX/mKpf5mGqv45b8W+1rSStBCYBv+WJPwhse3JzWaXADJSk3YHTgc5opHuBQ9q0RlrpY3pB+XqT7Tb89fu4tl9f20j6HkPvPoew/bYBpjPupIlssE4EPmL7YgBJrwe+Cry6wZz6plzPfKoOYwHbS5pt+9IG0+qbNl+fpL2oRlo9l6GjkJ7XVE598i9NJzAokt4GvLZ8vcT295vMB3IHM1CSrrH9krXFxipJVwF/Yvum8v0FwBm292g2s/5o8/VJ+iXwYeAqukYh2b6vsaT6rNx97kJ1R3NTeZR6K0g6BngF8I0Seidwle3Dm8sqdzCDdouk/0fVTAZwMHBrg/n02/qdX74Atv+7e2JpC7T5+lbYPq/pJOpShpZ/GfgfqrvPHSX9eYuu+c3AS20/BiBpPvBzIAVmHHkv8HGqeQUCLgXe02hG/bVQ0kk8UUDfRfUXcVu0+foulvRpqn+bj3SCtq9uLqW++gzwBtuLASQ9H/h3oC0FBmBzYHn5vFmDeTwuTWQNkDQZeKxtI3PKkNZDeWIk0qXA8bYfGfHAMaLN1yfp4mHCtv17A0+mBpIutf3aru8CftQdG8skvRM4BriY6t/ma4EjbJ854oF155UCMziSfodq9nf3KLLZtq9rLqv+Ku3cL+SJdu5WjbJq+/W1jaQ/Kh/fSDWA4Wyq/3YHUf33++umcuu3srbhK6gKzOW2lzacUgrMIEn6KfCxnlFk/2S7taPIqAromB9lBe28PkkH2/56z2oTjxvrq0xIOmWEzW56ra5nStIutn8p6eXDbW+6iTN9MIM1qVNcAGxfImlSkwn12WeAfXpHWQFjfpRV0cbr6/z7a8WqEr1st6mPczgfAeZS/dvsZaDRJs7cwQyQpO8AVzN0FNkM2wc0llQfSbrW9ovXFhur2n59bSRpnu1jh1nuB6Aty/wgaSPbD68tNmi5gxmsjCIb21p7feW5Nofx5OXex/pM9xvL+8JGs6jfT6ket7C22EDlDqYBkjajGkW2sulc+qnNo6yg3dcn6RrgJGAR8FgnbvtHjSXVR5IOsv3NtcXGGknbAVOBrwN/QvXvEqoFWb9se5emcoMUmIGS9ArgZJ5o714BvNd2K/4KhsdHWb2I6pdUq2ZLQ3uvT9Lltl/ZdB51kXS17ZevLTbWlIVY/xSYwdC7tJXAqba/3UReHSkwAyTpWuBQ2/9Vvr+G6i/gVrThDzdbmuqhaq2YzNbm6yvPStmZ6lk3rZloKelNVLPc/xg4q2vTZGBX2zMbSazPJL3d9reazqNXCswASfqJ7b3WFhurynpWb+mdLd30bXq/tPn6JP0z8G6q4tlpIhvzEy0lvQR4KfAJ4B+6Nq0ELrZ9fxN51aH8AbQbsFEnZvsTzWWUTv6B6BqjfoWkr1ANbTXVQ50uaSqvGtzT+eVb3ALc01QyNWjz9f0h8Ly2NPl12L5G0nVUw8vH/HN71kTSl4FNgDcAXwMOBK5oNClSYAald4z6kV2fx/wtZNds6esl/YChs6WvbCyxPmn79RXXUK1l1ZaC+TjbqyVtJWmDthXQLq+2/eIybP7jkj5DNVq1USkwA2D7DU3nULO3dn3+NfC68nkZsMXg0+m7tl8fwLbALyVdydA+mLE+TLnjV8BPJC0AHuoEx/pKBV3+r7z/r6TnAPdR9RE2KgVmwEZjO+kz1fbZ0p3rk7SX7Z90bysP6mqDI9e+y5h2V3mtRztXLfi+pM2BT1NN5jZVU1mj0sk/QGtqJ7U9p9HE+qQsnXICsK3t3SW9GHib7X9sOLW+aOtQ1/FE0iTbD619z7FF0oad+VhlvtZGwMNNz9Far8kfPg692vYhwP22Pw68imrBxLb4KnAE8CiA7WuBWY1m1AeSXiXpr4Epkj7S9ToKmNBwen0haU9JV0r6jaTfSlot6cGm8+qX8t/wBsrMfkkvkXR8w2n102WdD7Yfsb2iO9aUNJENVmddoFHVTtpHm9i+onrUxuNWNZVMH20APIvq/y/dzSsPUt2FtsGXqP4Y+CbVpL1DqObFtMXngH2BBfD46LIx/yyYrpn8G0t6GUNn8m/SWGJFCsxgfW+YdtKvNppRf91b5oYYQNKBwN3NpvTMleVSfiTpVNu/amszi+3FkibYXg2cUh4v0Rq27+j542d1U7n00b5UM/mnUY1W7VzgSuDvGsrpcSkwAyJpPeBC2w8A35L0fWCjcivbFocCJwK7SLoTuJVqQci2eI6k86juZnYok/j+3PZfNJxXP/xvWQbnGknHUv1h0KZHSdwh6dWAy3V+kCcWwhyzytye+aN1Jn/6YAbE9mN0zYfpaidtE9v+fWAKsIvt19Cuf2Ofo/qL8T6omlmoHk3bBu+m+m91KNUw3mnA2xvNqL/eT3VtU4ElVLP7D20yoT6bJmmyKl+TdLWkfZpOqk3/5x8Lzpf0dvXcp7fItwBsP9S1UvQ5DebTd7bv6AmN6WYWSftLOtT2r8qzQy6ganL5Q6pfwm0h2++yva3tbWwfbPu+ppPqo/fafhDYB9iG6jEgxzSbUprIBu0jVM0OqyQ9TNVeatuTm03rmZG0C9Xcns26Zr1D1dG40fBHjUltbGaZx9CRfhtSPaHzWcAptOcPhJ9KupVqwctvlabqNun80fpm4JQyiKHxP2RTYAbIdhsneAG8EHgL1VIj3bPeVwLvayKhmrwf+DxPNLOcz9hvZtmg567sx7aXA8vVosd5295Z0kyqYvqxMmT5TNtfbzi1frlK0vlUo1KPkLQpXc/1aUomWg6QpAtt77222Fgl6VW2Gx97H+tO0mLbO61h2//Yfv6gc6qbpK2BzwLvst2WeUzrUTVp3mL7AUlbAVPLXLTG5A5mACRtRDUmfWtJWzB0rPpzGkus/+6Q9B1gL6qhyj8GPmR7SbNpPTOS/mGEzbb9yYEl03+XS3qf7SHD5SX9OaNgNd5+kTSZql9pFvB84DtAK54FUxxlu/vf6QPAF2h4FGfuYAZA0oeAv6IqJnd1bXoQ+KrtLzWRV79JugD4N554Zv3BVH8lvrG5rJ65Mou/1yRgDrCV7WcNOKW+kbQN8F2qBS47Dxfbg6ov5gDbv24otb4q/S/fBc5u4122pFOpnrD6z2WpmG8CV9s+qtG8UmAGR9Jhtr/YdB51kXSN7Zf0xH5h+6UNpdR3pW37Q1TF5WzgM7bH/BL3kn6PaqAGwPW2L2oyn36TJLf4l13p0P8GsIhqrcPzbB/XbFYpMAPRM7LqSZp+bna/SPpP4FSqB6oBvBN4Txv6mCRtSTUK8F3AfODzbXoaYtuVhVg/Ckynq2ugBU/s7F5odX3gK8BPgJOg+Udep8AMgKRTeOLBYr1DB237vQNOqRaSdqBa0+pVVNf7U6o+mF81mtgzJOnTwB9RrVLwr7Z/03BK8RRJugb4MnAVXXOXbF/VWFJ9IOniETY3/sjrFJgBGKYN31QPq/qx7VsbSCmeAkmPUfVRrGLoE0hbMY9pPJB0le09ms6jDmUE2UG2z2o6l14pMAMgabiHOW1JtezIUbbPHHBKfdXyUVbRAuXRCvdQjR7rfmLn8qZy6idJl9oedcsWpcA0qLTr/+dYf2BVm0dZRTuUUWS9bPt5A0+mBpL+H9Vjk89i6COhGy2gKTANk/Rz2y9rOo9+aesoq4jRbLQW0Ey0bFAZGtqKkUjDjLJ6eUZZxWghaX3gAzyx+vUlwFdsP9pYUn1ke1Q+uDB3MAMgaRFDO4eh6oO5CzjE9i8Hn1X/ZJRVjHaSvkY1jHd+Cb0bWG37z5rLqn9GawFNgRkASc/tCRm4ry1PRcwoqxjt1jAJ+EmxsWq0FtA0kQ3AWJ8Hsja281yhGO1WS3q+7f8BkPQ8xvizfHq8oqdYXlTm/jQqBSYixoO/AS6WdAvVnfVzqR7K1RajsoCmiSwixoWyCOQLqQrML20/spZDRj1Jf0W1NMwWwFeBzmiy6VRPuWx0TbkUmIhoLUkHU/2eO70n/j7gIdv/1kxm/SHpX4BXAy8C/hu4k2o5nFNs3zXSsYOQAhMRrSXp58Brba/siU8GLm7L8jHlEd4zqIrNq8rrAdu7NplX+mAios0m9BYXANsPlqG9bbEx1QMMNyuvu6iW7m9UCkxEtNn6kib1TgkoK05s0FBOfSPpRKrn+KwELqdawfyzo2WSc4aXRkSbnQScI2l6J1A+n1m2jXU7UD19dClV/8sSqscljwrpg4mIVpP0fuAI4FlUE4EfAo6xfUKjifVJeZrlblT9L68GdgeWA5fZHm4l98HllgITEeOBpGdR/c57Up9MG0iaBuxFVWTeQrWS+eaN5pQCExExNkn6IFVB2Qt4lGpOzGXlfZHtxxpML538ERFj2HTgHODDtu9uOJcnyR1MRETUIqPIImJckvTGpnNou9zBRMS4JOl22zs0nUebpQ8mIlpL0oI1bQK2GmQu41EKTES02e8CBwO9T1kVMHPw6YwvKTAR0WY/A/7X9o96N0i6qYF8xpX0wURERC0yiiwiImqRAhMREbVIgYmIiFqkwERERC0yiiwiWk/SXsBRwHOpfu8JsO3nNZlX22UUWUS0nqRfAh8GrgJWd+K272ssqXEgdzARMR6ssH1e00mMN7mDiYjWk3QMMAH4NvBIJ2776saSGgdSYCKi9SRdPEzYtn9v4MmMIykwERFRi/TBRERrSTrY9tclfWS47bY/O+icxpMUmIhos0nlfdNGsxin0kQWERG1yEz+iIioRQpMRETUIgUmIiJqkQITEa0maRdJe0t6Vk98v6ZyGi9SYCKitSR9EDgXOAy4TtL+XZv/qZmsxo8MU46INnsfsIft30iaDpwjabrtz1OtqBw1SoGJiDabYPs3ALZvk/R6qiLzXFJgapcmsohos6WSXtr5UorNW4Ctgd9pKqnxIhMtI6K1JE0DVtleOsy2vWz/pIG0xo0UmIiIqEWayCIiohYpMBERUYsUmIhnQNJWkn5RXksl3dn1fYNneO7XS7Kkt3bFvl9GQkWMehmmHPEM2L4PeCmApKOA39j+lz7+iCXAx4Dv9fGcEQORO5iI/tpY0q2S1geQNFnSbZLWl3SJpM9J+qmk6yTNLPtMknSypCsl/bxntvk1wApJb+z9QZL+oRxznaQTJanEL5F0nKRLJd0o6RWSvi3pZkn/2HX8wZKuKHdbX5E0od7/aWK8SYGJ6K//Ay4B/qB8nwV8y/aj5fsk268G/gI4ucQ+Blxk+xXAG4BPS5r0xCn5R+Dvh/lZX7L9Ctu7AxtTze/o+K3t1wJfploq5VBgd+BPS7Pei4B3AHvZfimwGnjX07/siCdLE1lE/30NmAd8F3gP1XIlHWcA2L603N1sDuwDvE3SR8s+GwE7dA6w/V+SkPS7PT/nDZLmAZsAWwLX80RT2oLyvgi43vbdAJJuAbYHXgPsAVxZbnw2Bu55ZpcdMVQKTESf2f6JpOmSXke1VMl13Zt7d6dasuTttm/q3iBp266vR1Pd6awq2zYCjgdm2L6j9P9s1LX/I+X9sa7Pne8Ty8+cb/uIp3GJEeskTWQR9TiN6m7llJ74OwAkvQZYYXsF8EPgsK4+lJf1nsz2+cAWwEtKqFNM7i3L0B/4FPO7EDhQ0jblZ25Z1ueK6JsUmIh6fIOqIJzRE79f0k+p+kbmlNgngfWBayVdV74P52hgGoDtB4CvUjWBfRe48qkkZ/sGqn6d8yVdC1wAPPupnCNibbJUTEQNJB0I7G/73V2xS4CP2l7YWGIRA5Q+mIg+k/RF4E3Am5vOJaJJuYOJiIhapA8mIiJqkQITERG1SIGJiIhapMBEREQtUmAiIqIWKTAREVGL/w9TVBdYqAnKlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['TypeName'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226903ea",
   "metadata": {},
   "source": [
    "Lets have a look at the density of `inches` - which is the screen size of the laptop. And the winner is `15.5 - 16 inches`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e2c6cd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:58.520807Z",
     "iopub.status.busy": "2022-11-16T06:00:58.520336Z",
     "iopub.status.idle": "2022-11-16T06:00:58.784550Z",
     "shell.execute_reply": "2022-11-16T06:00:58.783117Z"
    },
    "papermill": {
     "duration": 0.2961,
     "end_time": "2022-11-16T06:00:58.787820",
     "exception": false,
     "start_time": "2022-11-16T06:00:58.491720",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiva\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Inches'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710aba90",
   "metadata": {},
   "source": [
    "We defined a seperate column for `Touchscreen` and visualized it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f875aee7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:59.242061Z",
     "iopub.status.busy": "2022-11-16T06:00:59.240874Z",
     "iopub.status.idle": "2022-11-16T06:00:59.248387Z",
     "shell.execute_reply": "2022-11-16T06:00:59.247536Z"
    },
    "papermill": {
     "duration": 0.041048,
     "end_time": "2022-11-16T06:00:59.250837",
     "exception": false,
     "start_time": "2022-11-16T06:00:59.209789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d7950e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:59.394266Z",
     "iopub.status.busy": "2022-11-16T06:00:59.393128Z",
     "iopub.status.idle": "2022-11-16T06:00:59.568131Z",
     "shell.execute_reply": "2022-11-16T06:00:59.566625Z"
    },
    "papermill": {
     "duration": 0.20783,
     "end_time": "2022-11-16T06:00:59.571126",
     "exception": false,
     "start_time": "2022-11-16T06:00:59.363296",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Touchscreen'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68792197",
   "metadata": {},
   "source": [
    "What effect does `Touchscreen` have on the price of laptop? They are more expensive!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "12ed0dc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:59.638957Z",
     "iopub.status.busy": "2022-11-16T06:00:59.637861Z",
     "iopub.status.idle": "2022-11-16T06:00:59.901913Z",
     "shell.execute_reply": "2022-11-16T06:00:59.900609Z"
    },
    "papermill": {
     "duration": 0.297793,
     "end_time": "2022-11-16T06:00:59.904414",
     "exception": false,
     "start_time": "2022-11-16T06:00:59.606621",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Touchscreen', ylabel='Price'>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Touchscreen'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7aa9f1e",
   "metadata": {},
   "source": [
    "Since, we have the screen resolution and the size of the laptop in inches, we can go ahead and find the `Pixels per Inch` count, and check and see if this attribute has any importance to the price of a laptop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "1aa40a90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:00.649936Z",
     "iopub.status.busy": "2022-11-16T06:01:00.649054Z",
     "iopub.status.idle": "2022-11-16T06:01:00.658153Z",
     "shell.execute_reply": "2022-11-16T06:01:00.656969Z"
    },
    "papermill": {
     "duration": 0.043896,
     "end_time": "2022-11-16T06:01:00.660599",
     "exception": false,
     "start_time": "2022-11-16T06:01:00.616703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x',n=1,expand=True)\n",
    "df['X_res'] = df['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])\n",
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]\n",
    "df['X_res'] = df['X_res'].astype('int')\n",
    "df['Y_res'] = df['Y_res'].astype('int')\n",
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "af8da35c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:01.429335Z",
     "iopub.status.busy": "2022-11-16T06:01:01.428417Z",
     "iopub.status.idle": "2022-11-16T06:01:01.436464Z",
     "shell.execute_reply": "2022-11-16T06:01:01.435295Z"
    },
    "papermill": {
     "duration": 0.043997,
     "end_time": "2022-11-16T06:01:01.439109",
     "exception": false,
     "start_time": "2022-11-16T06:01:01.395112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution'],inplace=True)\n",
    "df.drop(columns=['Inches','X_res','Y_res'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46882068",
   "metadata": {},
   "source": [
    "Lets segregate, the CPU name and its brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f471aeed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:01.830380Z",
     "iopub.status.busy": "2022-11-16T06:01:01.829546Z",
     "iopub.status.idle": "2022-11-16T06:01:01.837504Z",
     "shell.execute_reply": "2022-11-16T06:01:01.836536Z"
    },
    "papermill": {
     "duration": 0.044491,
     "end_time": "2022-11-16T06:01:01.840054",
     "exception": false,
     "start_time": "2022-11-16T06:01:01.795563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "5405e6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_processor(text):\n",
    "    if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3':\n",
    "        return text\n",
    "    else:\n",
    "        if text.split()[0] == 'Intel':\n",
    "            return 'Other Intel Processor'\n",
    "        else:\n",
    "            return 'AMD Processor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5f27f987",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "17279e18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:02.879637Z",
     "iopub.status.busy": "2022-11-16T06:01:02.878858Z",
     "iopub.status.idle": "2022-11-16T06:01:02.885640Z",
     "shell.execute_reply": "2022-11-16T06:01:02.884727Z"
    },
    "papermill": {
     "duration": 0.044176,
     "end_time": "2022-11-16T06:01:02.888033",
     "exception": false,
     "start_time": "2022-11-16T06:01:02.843857",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e801dc7",
   "metadata": {},
   "source": [
    "The memory column of the data needed attention. It had a lot of different values, multiple memorys' and different kinds of memories. Standardizing all this data into seperate columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "ec0b7c27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:04.024319Z",
     "iopub.status.busy": "2022-11-16T06:01:04.023195Z",
     "iopub.status.idle": "2022-11-16T06:01:04.071402Z",
     "shell.execute_reply": "2022-11-16T06:01:04.070310Z"
    },
    "papermill": {
     "duration": 0.086261,
     "end_time": "2022-11-16T06:01:04.074281",
     "exception": false,
     "start_time": "2022-11-16T06:01:03.988020",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiva\\AppData\\Local\\Temp\\ipykernel_3756\\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\\shiva\\AppData\\Local\\Temp\\ipykernel_3756\\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', 'Memory'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f332ecfc",
   "metadata": {},
   "source": [
    "Similarly, we did it for `Ips` and checked it relation to pricing of a laptop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "abc3b778",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:00:59.966099Z",
     "iopub.status.busy": "2022-11-16T06:00:59.965146Z",
     "iopub.status.idle": "2022-11-16T06:00:59.973516Z",
     "shell.execute_reply": "2022-11-16T06:00:59.972050Z"
    },
    "papermill": {
     "duration": 0.041282,
     "end_time": "2022-11-16T06:00:59.976001",
     "exception": false,
     "start_time": "2022-11-16T06:00:59.934719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "dce0253b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:00.121724Z",
     "iopub.status.busy": "2022-11-16T06:01:00.120500Z",
     "iopub.status.idle": "2022-11-16T06:01:00.278267Z",
     "shell.execute_reply": "2022-11-16T06:01:00.277064Z"
    },
    "papermill": {
     "duration": 0.192193,
     "end_time": "2022-11-16T06:01:00.280741",
     "exception": false,
     "start_time": "2022-11-16T06:01:00.088548",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ips'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4298ae",
   "metadata": {},
   "source": [
    "Laptops with In-plane switching are more expensive than the ones that do not have it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3039ec15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:00.343771Z",
     "iopub.status.busy": "2022-11-16T06:01:00.343354Z",
     "iopub.status.idle": "2022-11-16T06:01:00.582633Z",
     "shell.execute_reply": "2022-11-16T06:01:00.581296Z"
    },
    "papermill": {
     "duration": 0.274129,
     "end_time": "2022-11-16T06:01:00.585553",
     "exception": false,
     "start_time": "2022-11-16T06:01:00.311424",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ips', ylabel='Price'>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Ips'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f777f48a",
   "metadata": {},
   "source": [
    "Performing the same setps above on the `Ram` variable, we can see that, higher the RAM, higher its price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "e8a9535a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:03.043797Z",
     "iopub.status.busy": "2022-11-16T06:01:03.043351Z",
     "iopub.status.idle": "2022-11-16T06:01:03.255840Z",
     "shell.execute_reply": "2022-11-16T06:01:03.254600Z"
    },
    "papermill": {
     "duration": 0.250141,
     "end_time": "2022-11-16T06:01:03.258421",
     "exception": false,
     "start_time": "2022-11-16T06:01:03.008280",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XV9UXRhDrpKra3y2P9xXUF5NsH0EeYHaWFvC7wEuq6gdJFgHXJllUVVcx+d3gx8KHgHuATwC/k+TXgTdU1aPAS0eU6VvANyeMnQncSu/f4nOOeaIRZjquT8sAJ3U3ST2N3tH7Yz/OzwNGdVpmnN4787uA71XVjcCPq+oLIypRgEuq6rHfY/EXwG9W1S8Av8zo3nTuSPKmbvm27k2HJM8F/m9EmaBXWqFXWiuTfCLJvO65UZXWnKr6AUBV3Q+8Anh1kvcyunL/+apaW1Wf7n4avRW4IckzR5QH4B30fkfV66pqcVUtBnZ3y6Mo9tFmqqrj9g/wh8C99N4Z3wZsBf4BuB1494izLQT+GXgf8MCIs9wFzO2WvzLhudtHlOkZwIeBbwA30Sv0e4Ev0DstM6rv1fYJ6+8CvgQ8E7h1RJluAM6fMDYX+AhwYESZdtL76at/bBWwA/jmCP/+Hvt39156B333jirLqDMd1+fcAZI8C6Cq9iQ5BXgVvTK9eaTBOkkuAS6qqneOMMNbgdcC64GXA6fws88mnlNVbxxhtqfR+9F0Lr0jmkdGlaXLsxM4t6p+2je2it4R2MlV9ewRZFoI7K+qhyd57qKq+tIIMv058Pmq+vcJ48uBv6mqJcc604Qcr6X3xryoqn5ulFkec6wzHfflrsEkeQXwe8Bz6RXpg8Cn6f1qiP1TbniCme2lNZskWQpUVX21+894lgN3VdW/jjDT8+md074JOEDv9NEdSZZX1WdHkOdCYGdVfT/JU4A/AV5M79Ttn1bV92bstS33E1uSN1XVh0ad43jg9+pnkryb3v+0NhfYAlwI3EjvJ+fPVdW6EWSajVcVTbwq7If0Ps+Z8avCLPcTXJIHqursUec4Hvi9+pkkt9Mr0HnAw8DC7uj0ycBNVfWiEWV6WfVdVQT8Y1VdleRrVXXBCDLtrKoXdMuPe4NJsr2qzp+p1z7eL4XUAJJ8faqngPnHMsts5/dqYPur6gDwoyTfqKrvA1TVj5P8dJptZ8rjrirqTkVem+TZjO6qojv6fuK7Lcl4VW07FleFWe4nhvnAxcD/TBgP8F/HPs6s5vdqMD9J8pSq+hHwkscGu7uLR1XuDyc5v6q2A3RH8K8BPgi8cESZ3gxcleSP6V3z/uUkD9L7zOvNM/nClvuJ4TP0rvTYPvGJJDce8zSzm9+rwby8ejcs0X9lEb37S475neGdy4HHXRzQXSxweZK/H0Wg7gPT3x7FVWGec5ekBh3vd6hKkiZhuUtSgyx3SWqQ5S5JDbLcJalB/w8Q2B2VJ3bmPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ram'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "3c05c7e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:03.328538Z",
     "iopub.status.busy": "2022-11-16T06:01:03.327647Z",
     "iopub.status.idle": "2022-11-16T06:01:03.870783Z",
     "shell.execute_reply": "2022-11-16T06:01:03.869536Z"
    },
    "papermill": {
     "duration": 0.580244,
     "end_time": "2022-11-16T06:01:03.873238",
     "exception": false,
     "start_time": "2022-11-16T06:01:03.292994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Ram'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4e6e9c3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:04.415683Z",
     "iopub.status.busy": "2022-11-16T06:01:04.415218Z",
     "iopub.status.idle": "2022-11-16T06:01:04.426154Z",
     "shell.execute_reply": "2022-11-16T06:01:04.424686Z"
    },
    "papermill": {
     "duration": 0.048692,
     "end_time": "2022-11-16T06:01:04.428413",
     "exception": false,
     "start_time": "2022-11-16T06:01:04.379721",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            1.000000\n",
       "Touchscreen      0.191226\n",
       "Ips              0.252208\n",
       "ppi              0.473487\n",
       "HDD             -0.096441\n",
       "SSD              0.670799\n",
       "Hybrid           0.007989\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking data co-relation\n",
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "2f2e0f65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:04.499537Z",
     "iopub.status.busy": "2022-11-16T06:01:04.499117Z",
     "iopub.status.idle": "2022-11-16T06:01:04.504833Z",
     "shell.execute_reply": "2022-11-16T06:01:04.503962Z"
    },
    "papermill": {
     "duration": 0.043855,
     "end_time": "2022-11-16T06:01:04.506955",
     "exception": false,
     "start_time": "2022-11-16T06:01:04.463100",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae5eef2",
   "metadata": {},
   "source": [
    "The `Gpu` column also needed preprocessing before we could use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "8d8da3b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:04.670246Z",
     "iopub.status.busy": "2022-11-16T06:01:04.669798Z",
     "iopub.status.idle": "2022-11-16T06:01:04.683285Z",
     "shell.execute_reply": "2022-11-16T06:01:04.682050Z"
    },
    "papermill": {
     "duration": 0.05247,
     "end_time": "2022-11-16T06:01:04.685651",
     "exception": false,
     "start_time": "2022-11-16T06:01:04.633181",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "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": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "3ae9250c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:04.762033Z",
     "iopub.status.busy": "2022-11-16T06:01:04.760788Z",
     "iopub.status.idle": "2022-11-16T06:01:04.768355Z",
     "shell.execute_reply": "2022-11-16T06:01:04.767405Z"
    },
    "papermill": {
     "duration": 0.045824,
     "end_time": "2022-11-16T06:01:04.770507",
     "exception": false,
     "start_time": "2022-11-16T06:01:04.724683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Gpu brand'] = df['Gpu'].apply(lambda x:x.split()[0])\n",
    "df = df[df['Gpu brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a8efc8f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:05.022936Z",
     "iopub.status.busy": "2022-11-16T06:01:05.022116Z",
     "iopub.status.idle": "2022-11-16T06:01:05.029000Z",
     "shell.execute_reply": "2022-11-16T06:01:05.028128Z"
    },
    "papermill": {
     "duration": 0.046111,
     "end_time": "2022-11-16T06:01:05.031482",
     "exception": false,
     "start_time": "2022-11-16T06:01:04.985371",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2248eac4",
   "metadata": {},
   "source": [
    "Lets look at the price of the laptops having different brands of GPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "57cd55a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:05.186167Z",
     "iopub.status.busy": "2022-11-16T06:01:05.185706Z",
     "iopub.status.idle": "2022-11-16T06:01:05.544177Z",
     "shell.execute_reply": "2022-11-16T06:01:05.543168Z"
    },
    "papermill": {
     "duration": 0.397346,
     "end_time": "2022-11-16T06:01:05.546526",
     "exception": false,
     "start_time": "2022-11-16T06:01:05.149180",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Gpu brand'],y=df['Price'],estimator=np.median)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "31c996db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:05.621357Z",
     "iopub.status.busy": "2022-11-16T06:01:05.620608Z",
     "iopub.status.idle": "2022-11-16T06:01:05.626830Z",
     "shell.execute_reply": "2022-11-16T06:01:05.625720Z"
    },
    "papermill": {
     "duration": 0.045948,
     "end_time": "2022-11-16T06:01:05.629761",
     "exception": false,
     "start_time": "2022-11-16T06:01:05.583813",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f806c51",
   "metadata": {},
   "source": [
    "Next up is `Operating system`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c056d2a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:05.868363Z",
     "iopub.status.busy": "2022-11-16T06:01:05.867683Z",
     "iopub.status.idle": "2022-11-16T06:01:05.876733Z",
     "shell.execute_reply": "2022-11-16T06:01:05.875602Z"
    },
    "papermill": {
     "duration": 0.04933,
     "end_time": "2022-11-16T06:01:05.878961",
     "exception": false,
     "start_time": "2022-11-16T06:01:05.829631",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "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": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a178857c",
   "metadata": {},
   "source": [
    "Looks like a tuff race between macOS and Windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "4373e203",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:05.954431Z",
     "iopub.status.busy": "2022-11-16T06:01:05.953964Z",
     "iopub.status.idle": "2022-11-16T06:01:06.372941Z",
     "shell.execute_reply": "2022-11-16T06:01:06.371628Z"
    },
    "papermill": {
     "duration": 0.459527,
     "end_time": "2022-11-16T06:01:06.375562",
     "exception": false,
     "start_time": "2022-11-16T06:01:05.916035",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['OpSys'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d22dca0",
   "metadata": {},
   "source": [
    "Since, there are multiple Windows version, we combined them into one, and then again had a look at the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "005b685a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:06.450636Z",
     "iopub.status.busy": "2022-11-16T06:01:06.449366Z",
     "iopub.status.idle": "2022-11-16T06:01:06.455560Z",
     "shell.execute_reply": "2022-11-16T06:01:06.454413Z"
    },
    "papermill": {
     "duration": 0.046263,
     "end_time": "2022-11-16T06:01:06.458137",
     "exception": false,
     "start_time": "2022-11-16T06:01:06.411874",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cat_os(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": 164,
   "id": "df45c4e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:06.533787Z",
     "iopub.status.busy": "2022-11-16T06:01:06.532482Z",
     "iopub.status.idle": "2022-11-16T06:01:06.539193Z",
     "shell.execute_reply": "2022-11-16T06:01:06.538262Z"
    },
    "papermill": {
     "duration": 0.046932,
     "end_time": "2022-11-16T06:01:06.541370",
     "exception": false,
     "start_time": "2022-11-16T06:01:06.494438",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['os'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "d6c8e164",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:06.711392Z",
     "iopub.status.busy": "2022-11-16T06:01:06.710953Z",
     "iopub.status.idle": "2022-11-16T06:01:06.718247Z",
     "shell.execute_reply": "2022-11-16T06:01:06.717345Z"
    },
    "papermill": {
     "duration": 0.048138,
     "end_time": "2022-11-16T06:01:06.720549",
     "exception": false,
     "start_time": "2022-11-16T06:01:06.672411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b75c42",
   "metadata": {},
   "source": [
    "Much better. We can see that mac is leading the way!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "a4965f3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:06.795626Z",
     "iopub.status.busy": "2022-11-16T06:01:06.794398Z",
     "iopub.status.idle": "2022-11-16T06:01:07.036067Z",
     "shell.execute_reply": "2022-11-16T06:01:07.034877Z"
    },
    "papermill": {
     "duration": 0.281761,
     "end_time": "2022-11-16T06:01:07.038635",
     "exception": false,
     "start_time": "2022-11-16T06:01:06.756874",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['os'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0415b299",
   "metadata": {},
   "source": [
    "Lastly, let us have a look at the distribution of the `Weight` of the laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "539f75e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:07.113172Z",
     "iopub.status.busy": "2022-11-16T06:01:07.112355Z",
     "iopub.status.idle": "2022-11-16T06:01:07.334323Z",
     "shell.execute_reply": "2022-11-16T06:01:07.333083Z"
    },
    "papermill": {
     "duration": 0.261704,
     "end_time": "2022-11-16T06:01:07.336848",
     "exception": false,
     "start_time": "2022-11-16T06:01:07.075144",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiva\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8951d521",
   "metadata": {},
   "source": [
    "Comparing it to the Price of a laptop, not much can be inferred from here to be honest, but there sure is an increase in price that are relatively heavier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "cdafe0ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:07.412819Z",
     "iopub.status.busy": "2022-11-16T06:01:07.411721Z",
     "iopub.status.idle": "2022-11-16T06:01:07.607471Z",
     "shell.execute_reply": "2022-11-16T06:01:07.606456Z"
    },
    "papermill": {
     "duration": 0.236457,
     "end_time": "2022-11-16T06:01:07.610201",
     "exception": false,
     "start_time": "2022-11-16T06:01:07.373744",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Price'>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8EuZPyiEy0lxPGAzJyL118kcIIaL1P4QQMUB0kP0VISTQCkeFvw5Ohko4tWJwYKZ5zJ2YYwgX8Aidm17cyrbqpoDnGQzac281mL8D64QQj+OJJLsKeDJko1IEJZBtdewg6A2hGFiUJnv8YaZ5RFgwNXfVNHcGPM9g0J57JWCklHcLIbYAswAB3CalVC2Iw4SKTFLA4IgSUhx7dM3D+3udkpdiau5KjYsKeq5w1y/rdbFLKaV3q2JFGBkMtlVF+Bmqmmx/ta7jXWsz0zwa2x0snlnEyvW7DKGzeGYRksFtFg8qYIQQH0gpzxBCtIDPJxGAlFImhnR0ClPMVjgqMunY0dcJLFwT3lDUZPurdZ0oWlt3zWPTvkZWl+3l6jMKEAKkhNVle/laYVqYRxqcoAJGSnmG9n/CwAxH0RsGg231eKWvE1g4J7yhqMn2V+saqlpbf3G4XFxamuenwThdwUOVw02PUWRCCIsQYutADEbRe1RkUmjKYPQ1OS2cyWz9jRIKRxmRQFpXZV1br64/GHI7wkFaXLShwVw7s5CrzyhgddleUuMGdzBvjz4YKaVbCLFJCJEnpdw7EINSKHoyO4VKc+ir2SmcZqr+aLLh0rwCaV2f7Wukw+nu8fpDUWs7FoxMi+PmuePZXNWEW0KkBW6eO37Qm8V7mweTDWwTQqwTQqzV/4VyYIoTl97k+YRKc+hraZdwl4I5Wk02XJqXmda1eGYRz5ZV9er6gyG3I1w4uiSr3qvg/vXl/O29Chxdg9vBD72PIru1rycWQjwGzAVqpZQl2rbfAz8EDmm7/VZK+ar23o3A1YALWKyHQQshpgJPADHAq8ASKaXUEj+fAqYCdcClen00IcQVHKkycLuU8rjI2RloZ3K4nNe9sbOHSnPoawDFUA24CJfmpWtdaVdO4/3yw0gJT2+spLrJY+Kqb+s0xmf2zPXX/zgUI9DcbsmW/Y0sf3274eQHWP76dsZmJQxq31NPUWQ24MdAIbAFeFRK2ds+ME8A9+MRAt7cK6W8p9t1xuNpYFYMDAfeFkKMllK68HTSXARsxCNg5uAJl74aaJBSFgohLgOWA5cKIVKBpUApnsi3T4QQa6WUDb0c96BkoE0a4XRe92byC5WppK8T2FANuDgW9+9oJ2uLRZCeEM0j71f4XD8/LYb9jXa+9+hHQZ+5o83tGIoRaPqYa5o6TJ38dW2dg1rA9GQiexLPRL0F+Bbwp96eWEr5HlDfy93nAc9IKTullLuBcmCaECIbSJRSbpBSSjzCar7XMbpm8hwwS3g6on0TeEtKWa8JlbfwCKUhzUCbNMLpvO6N2SmUppK+mp2GYsDFsQgQ6E+5IrPr3zZvAtc/vzlkz9xgqC7cW/QAjHd31vLlwWYKMxMM4QKesa9cvwtB8GdtsPeDGS+lnAAghHgU+OgYXPNaIcTlQBnwS00I5ODRUHSqtG1O7XX37Wj/7wOQUnYJIZqANO/tJsf4IIRYhEc7Ii8vr3+fKsQMtEljoK/nvRrOSLBx/3dO5tp/fhbQ7DRUNYfBQn/vX3/Dhc2uH+pnbiCf6f6Y4sw0rVHD4k3HXt/m6NN5Blpj60nAOPUX2iTe3+s9CNyGx3R1Gx6N6CowFcMyyHaO8hjfjVKuwtNIjdLS0kHtMeurSaO/tuaBjNYJ9EN4fckMDjYHnvzCXQZjqNOf+3csJmuz64fymRuoZ7q/E7uZ8N59uNV07JmJgcOUB0POUE8msklCiGbtXwswUX8thGju68WklDVSSpeU0g08DEzT3qoCRnjtmgsc0Lbnmmz3OUYIEQkk4THJBTrXkKYvJo1jUW15IKN1Av0Q3JIhZXaC8JskBopQRM+F+pkbqGe6v6Y4M+G9pqyK2+aV+Ix92bwSxmUGLqYyGHKGesrkjziWFxNCZEspq7U/LwD0BM61wD+FECvwOPmLgI+klC5NmE0HPgQuB/7idcwVwAZgAbBeiy57A7hTCJGi7TcbuPFYfo5wYGZSyEuJNbSU2KhIHC4XaXHRSEm/Vy4DaYIKpeliIKOGBoNJYqAIRfRcqJ+5gXqm+/s8m2laDe0OGto6fUrFPPDOLkrzUwKeMyPBXGNLjx+4nKFeF7vsK0KIfwFnAcOEEFV4IrvOEkJMxmOy2gP8CEBKuU0IsQb4AugCrtEiyAB+wpEwZe+Cm48CTwshyvFoLpdp56oXQtwGfKztt0xK2dtgg0GNt0nBbDJbPLOI1WV7+fXssQGzpY9mopUhXoQfrekiXMmYgRgMJomBIlSTdajNngNhVjV7nvPTYoixRrDhq8M9/v7MhPedF0zgj298aYRz6wQTWhEWWDKriPvWHYk8WzKriIjeZj8eA0ImYKSU3zbZ/GiQ/e8A7jDZXgaUmGy3AxcHONdjwGO9HuwQxGwyW7l+F1efUcCu2paA2dLPllVxcWkuozMSGJedyKhh5g/6QE7OR7Ma7s34BnrCH4rFJ/uD8oGZ0/15zk+L4Wczi7h01cZe/ZbMhLdFeLQYb3pahFU32XlqQ6WP1vPUhkpOzktm5LCB+c5CJmAUoSXQZCaEx1575wUT+K1Xe9Uls4p4bUs1C6fn+8TSB3rQB3Jy7sn8Z7biC2cyZiBO1DImCl+6P88x1ghDuEDvfkvdhbfbLfu8CMtMtNHQ7uCBd8qNbQP9PCoBM0QJNJmNyUwgKlIwJS+Z//vZDPbWtxEZYeE3z23mwim5frH03g+6t8mpw+ka0Mm5J/Nfd0EYzmTMQAzVrP4TgXBl8EsJdW2OPv+WzMbbV5PkYHgelYAZopg9PItnFnHPmzv42cwihifG8PaXtVy35nN+MKOAhnYHQpi3Xa1tsTMyLc5nUr9hzpiwOQh7o530RngM9A9M5eYMTsJdBWPJrMI+pxgEGm9fTJKD4XlUAmaIoj88OYums25HLS73kZpON724ldEZ8cYD+vwnVSyeWURnlyug0Og+qUvC5yCsabaTEhvFhVNyjbpLz39S5bPi643w6MsPrDcBA31ZAXsHRoRy9TwUa2sNNAPti+t+vTVlVX6/pWALnUDjHfOzGQgRuE6bGeH2kykBM4SxWATtDhcr15X7bLc73eypayclNorqJjvVTXae3ljJ5aflc/v8Em56cauP0Nhd10pSjNVH8LQ5XDz/SdWAOQi9J8q46Eh+8vUC7nxth884sxKPrPh6Kzx68wPraYXb2xWw2X73f+dkHF0y6LFHKyR6GldXl5tt1U1UN9nJToqhODuRyMgBDCEaJIS7CobubH/yymlIZI+aRKDxbj/YzK+e3TSkQuCVgBniBDIVlR9q5eLSXEP4VDfZuW/dLp778WksOrMAtwSbNtlsrmri60Xp5KfFUFnXYZxnoByEZhPlkllFhoC0O93ct24Xs8dn+RzXG+HRm0m2pxVub1fAZvttrmpi1XsVAY/tj/km2LjyUmJ5cdN+n8XE7fNLmD8p54QTMpmJNvLTYpg7McfQiF/etD9kvrhAeSzpCdFHnQdjs1rYWdMy5ELgT6wn7ThkZFocd14wwbS/xujMBL+s5RZ7FyvXlfP8J1VYhOC+dbtYua6c7z76IT+bWUR+Wgzg+QHePr/E7/iByuS/b90uLpxypIiD3enmUGvfMpC7uty8uGk/l67ayI///imXrtrAi5v209XluzrsKeO5txnRZvu5JaTERnHN2YVcO9PzLyU2yji2P1nfwca1rbrJEC769pte3Mq26qYez3u8kZcSy89mFvHoB55eKo+8X8HPZhaRlxIbkusdTcUA7woQFoHf8XdeMIFny6p8jtG/68FcPUJpMEMci8UTMaZrJXp/jYZ2B+OyEnm1mwlpb30bi2cVkpMUw4GmDh8t4aYXt7J60XQ6nC4yEmzkJsVQlBHvs/ofyEx+79J3R6M9BZpkizLimTTCU+jB7ZbERkUGdcJ2X1FmJ9m4uDSXdoeLikOthrnDbOWZGB3B5afl+/mydHNff8w3wQIdvFe73uc92GRn0ojuZzq+2dvQbvocTMkLnAXfH/rqXA9kWv2/n83gUGvwPJisRNugrh6hNJghiveqxeWGiblJPPJ+BQ+8U05Du4MVl0xm1LA4nzLybrdkk2ayuf7fW/jbexUsnJ5PdpJnsrM73XQ4XUwvGEZeSiwb99SzbkctWw80s/iZT3lze02/V0dmqy3vulbZSTauObuQxbMKGZuVQHaS7ai1J11weqNPsvpYXt92kMXPfMrimUUBV5zeK9LsJBuXn5bPqvcquOqJMp86b2Yr1/E5SYZw0a9/37pduLRh9aemV7CVcnZSjOl5s5JOvJyccNTk6t7CAQioZeypazOaiV07s5AfzCjgtle+QIgjtfjyUs2/a5fbvCyUrgGHW7tRGswQxGzFc/dFE/nH1afSbHeSlxrnl6Hvdkv+V1Hn129Dz/5/4J1yY2JzuyX/t7Xa2Fc3u/W3g14gf8PscZmsuGQyy1/f7tdU6c4LJjAlL5m81L5HR+mTbPcVvj7Jepunnt7oyXiOsMCssRlMyEk2rue9Ij3U0skVj38U0Bbe2xL0h1rtnJQR369Q6mAr5eLsRL+AjtvmleCW0kfrOhEIdwJsT362urZO02Zi9V7NxAJ91x/urut16kE4tBslYIYQerTRnro2vjzY7GPe+s3zm7n6jAIe/aCCFZdMJj/VNxNeSiirrDd9GBNskT4T2566NlNB9McFk3we+r4SyN/w6uIZnpDrZJtfxvNvX9jCq4tn9DqqSv/M2Uk2YqMiWH7RRCoOtbKmrIqGdge3zy+hODsJ8F3ZVjfZjYCG009KCxiN1pNJ62hK0I/JTOCv351CXHQkmQnRRyVMu9eLi4y0MH9SjmHiTIqx8qc3d1BW2TTozCihJtwJhz0FiURFWEybia1eNN3nPGbPVjDhORhq4ykBM0QIVNxSz32xO91ER3oetOWvb8fpcvtoIPcsmOSJHDPL/s9K4PUlM4yJLdAkuqu2BbeUTHHLowqx7WlybneYVw+oae7ZH+F9f1Jio/z8HrfPL2FsVgJjM49EkR3NyravxwSb3AKtbPNS+9ZVMtAKNTLSwqQRKSTYWjl35fshnWgGcz5OOBIO+1IVI9Bz3+5w0RPBnq9g2s1g6QejGCQEKm6pR1rZrBYKM+LJTrIxd2KOnwayq7aFlzft9/M13Dx3PL9fuxW3xPjBBfILuNxw/fObfey7fek705O/QXe2d38/NiqiR1uy9/25cEqun9/jphe3EhsV6ROiezTRPn09Rp/cXl08g2cWnWpoaxaL6HffkN4eH2ofxLHoPxRqBrKtdff7sWlfY9DnPtDvIjOxZxNesOcrFD17+orSYIYIwSKtvH0kF07JJcLiXxJmTVkVv5w9hr+s32n4GsZmJfLQu+VU1nX0mCWva0veK6C+quA9mSocLheLZxb52aKl9NVOzKpBe9+fYCVxvMd1NCvboz3GLF+nvwmAvT0+1D6IwWCKGUz0NZN/ZFoc93/nZDZXNeGWECFgQm5Sr014gZ6vcJsGQQmYIUOw4pZXn1FgmMoiLHBKfqppotfUvGTuunAiGyrqcLnhtle+oLrJ7jfZBCtD471vXyfInibntLhoVpft9akesLpsL18rTDOES6Bq0N2bKwUqiWM2pr6W0jhW5Tf6O/H3dLxupqlr62T5RRN9TKbHcqI50doU9MTRZPI7uqSRkKt/P/1lMNQiUyayIYKZaeaWueOJirSQYIvgoqm55KfFMGtsBqcVpJmacfLT4jglP5WT0uN59IMKQ2AEmmySYqyMTIvz29ciYMNXhwOatIJNkMFMFSPT4rh57nij3lmSLYI7Lphg+JgCVYPeU9dmNFeyWS08/0mV8Vof00A3WuoNwcxtvQkv7el43Uxz8UMbWfHWl6xaWMq/fuhrRjkWDAZTzGDC7H7omfxmz31/TaXBGEjToBlKgxki6KuRMT+bwfaDzeysaeEv6z05L784ZzSJ0RFc940xxEdFBl25WCyC8yYOZ1JuEjXNnbQ5usjv5lTu7jBfdGYBozMTGJuZwJ76Nubc53EY56fF+IXCHquVcVSEhRFpcfzuhS2cNykHm9US1PSl10rTtR+Aa88uJD0hmr31HQPWaKkvzu5A3xMQMJx7b0O7ce68lFiiIoWRZGsREBVp3mytsq6DRU+X8epRmq3MPpd+nZpmOw8vLOWml7ZQWdcRFlPMYKKvpqneaoCDOZAiEErADCEsFoEQGAXvdO59eyeLzizgzmc+9zEbBTPjfFHd4vcDOGdMBttrmqlusvuEQa9c58mRWb1oOre98oXPJP7MR5U+2f/9eej31rexq6bVx1SweGYRr2+tDloNWl8pm9VOu/bsQgAuLs0lxhqJ2ysC7lhzNHXFzMxtFYda/Va0ZpGByy+ayIq3vvSpH2ezWnh18YxjarYK9LmiIgXX/vMzn/HkJNtIjYseEpNfqOiraao3ptKBbjlwrBhkRgMFBM++DTRx6LuYqdfdz2c2gV235nP+W3HYqNtlluWvJ4R513SaOTYLp8t9TFTwmuZOv+ivlet3MWN0Bk9vrMRmjQhYH83MXHTdN0aTEB3Jox9UsHJdOZeu2tCr6KajzX4+VqYOs+/YLDLw+uc3M3dijs9+uhDRfVLeBPJD9USgz7W5qslvPKlx0abPQbgzygeavpimehOZGEozWihRGswgo6eVSqDVjpSeMit6D5WDzR3kpcRisQi/8y2/aKKpkNp2oNlHO1ldtpcLp+QaWf6JtijzhLAf+iaEHS1tjq6AkXJ6NejXl8ww6qulx9uIsMCHu+vITLQxe1ymz3sdzi4WPLQhaHRTd7NDXkosb26v6dNKUT9HoPpffdUazL5js8hAu9Pt51fSV74Wcez6+fS0qPHeZvZZh+rqe6DojcYzVAMplIAJM90nOIswry2kT4pm9t0ls4p4bUu1T4TVI+97kgvHZSX4na/iUGvAiLQtB46ESl51+ig6NLPUiksm4+hymz7kVU0dTHAn92qyCGZHzk+JDSg8vZMQLRYRtAyG/oPb8NXhoD9Ks4lPNzv1NuTW+xw/mFFwTMrCm33HZpGBNquFUq/t3ZPsXttSzd0LJtHR2UVsdCQPv/fVUfmhAi1qun/dgRz7Koy5Z/9JT5GJ4S53c7QoARNGzCa4Oy+YwOiMeGYXZ5EeH01sdCT7G9uNEi3eq52aZjtRkRYONHZw1tgMP+3iphe38tD3pnqc3fHRJMdF4XZLKuvaeeA7U1j2yjbDKXvnBROoburw8X8smVVE6cgUXl08wyghY/aQf3mwhXFZiX3KtjdbyUZGCpaeV8ytL28z3l82r4TMxChmj59OcXaS8aM0m7SWv76dzIRoDrV2kp0Uw/Bk3z4g8dERSIlRBdlMmF+vldzx9uUEWyl6j+O9L2v58ZmF3PrKkfHfPr/kqMrCd3feu3GbOo5PL0jzq5htsQiyk2x8a0I2v3luk8/3mdWL5L3umAm8P108mWirMBVu3Rmqq+/uhKoxXG8YDDktR0PIBIwQ4jFgLlArpSzRtqUCq4GRwB7gEillg/bejcDVgAtYLKV8Q9s+FXgCiAFeBZZIKaUQIhp4CpgK1AGXSin3aMdcAdykDeV2KeWTofqc/cG8D8pOfvz1Qp9JdsmsIg63OgwHtb6C33HQ46gfnRHPD888yfRH3NTh5P53yk3Lp9w8dzwtdiftDhc5yTaueHxLt7Hs4pHLS30SMO+8YAK/fWGLcY5fnDMat5R8Ud1Mi73LKOlv9kPsaSV7sKmTh/5T7pMH88A7u5g7Mceosab/KLtPWtlJNi4tzeO7j37oM7n/avYYfv3cZtPPf+cFE4xABu97FsjsZIb3OGaMzjCEi36uoykLv6euzXCee4/B2zzoHXEG/rXIXG5Mqzh3b9rWW7oLvGir4KyiDFPh1p2huvr2JlSN4Xr7XAyGnJajIZQazBPA/XiEgM4NwDop5V1CiBu0v68XQowHLgOKgeHA20KI0VJKF/AgsAjYiEfAzAFewyOMGqSUhUKIy4DlwKWaEFsKlOJpLf+JEGKtLsgGE4GcubpwgSMTgx4qrD+Quw+3GWHEc0qyKa9tMf0Rf3WoFbvTvHzKba984alRhqSpw9z/0dbp4q0vDlKQFseo9Hif3jO2SAu2SAt/eH2Hz6SenhDFj57+1O+H2NNKts3RRWVdh4/2AEcy871/lN0nLbMcmZte3MqiMwsCfv7fvrCFRWcW8GxZleG7ihAwvSDNL+kt0ErRexy9rSAQDH2VbHaeg812n/LvwSa92pbgVZz7QiCBp4c89/TZhurq25v+CIljpcEdqwTfgSRkUWRSyveA+m6b5wG6NvEkMN9r+zNSyk4p5W6gHJgmhMgGEqWUG6SUEo+wmm9yrueAWUIIAXwTeEtKWa8JlbfwCKVBh1lCViBnrlviUzuqsr7NmDhXrt/FmrIq0zpjz5ZVkZ1kY2xWAj+Y4ek34R0ZtrO2hcX/+pxtB5rIT4vx6byYnxaDlJIfPvUJ/+/+D3ht60Fyk2MZm5XII+9X0OF0G8JFP99NL26lpcPl90PcU9fWY0Jefmqc6fvSK0JOvwfdO3kGu28QuHzMmKwELj8t34iM+9t7FdS3OXl9iX9tJzPMIoACfb6e0AVGT7WrdIJFFgW61wLR5yiuQBNkTXPvapkFq5c1VOhPPbcTORF1oMOUM6WU1QDa/xna9hxgn9d+Vdq2HO119+0+x0gpu4AmIC3IufwQQiwSQpQJIcoOHTrUj491dJhNTroz1xvdoer9QMZFRZKfFmMIjoum5vL61mqjadEfF0yixe4kKlKwcHo+v35ukxFarIcfe5zEKTzwnZMZnhTNT88q9AlB/vHXC6nV2hTbnW5++eznVDW2G+XlTx2VQkpslM9Y7U43bY4uv216fwpPRYEYo6nYwwtLDR/FqGH+92PxzCL+/WmV8bd+D7w7eV47s5CijATT+xYXFeHzd/f3c5Ni/DSbu9/YTlOH08fsFCjM1mIRzB6XyepF05mSl8QfurWvXnHJZPJSYnsM0XW7JVv2N7LjYDNRkRZunDM26Hn2HG7lcGun6aLB+157n2PJrCJ+vvrzPhejDFaEtLeEO6O8v4SqMZzO8RrGPVic/GZPmwyy/WiP8d0o5SpgFUBpaemAf6NmdtW8lFjTKLGizHifBzIrKZoff72QX3s5cfWClA3tDq4+o4B/f1rF7fNLuOafn/pMoCvXe0xuMdYIrn9+Cw3tDm6eO57/7PCNOnryfxX8cMZJxjVHZ8Tz6d5GHx/MkllFPLWh0vBjeCZ138fKCJ21CM4Zk0FsVASf7m3ALeGml7Zw/ZxxxorWO4DB6ZLc/NKWgCVt8lLjGJuVaJgKzcJyTxmZwv3fPhmX281t80q4+SXfqgMOtycyTg/xjo60UJQRz+JnPjMCIO7/zsk4uqSpKQrwCWvOT4th1cJSrBGi12HPZqauX5wzmiWzimhzuJhROIypeSnGecz8Sd7fvX6v9XtZWdfGZ/safb6n69Z8Ts6i6bQ7XD06rAMVIW2xO9nw1eFBm1V+LDPfQ9UYTh/n8RrGPdACpkYIkS2lrNbMX7Xa9irAu1N4LnBA255rst37mCohRCSQhMckVwWc1e2Yd4/txzh2mNlV9ZIwe+vbiI2KJDPRvwmVs0v6+Wq8BYc+mTS0O0xV+9zkGO59e5cx4bzw6T4uLs3ziTpaOreYKKswVseLZxVx7b8+87mm7h/Ss/1vn19CQkyEaXSR2y157YuDQTtlet8Pt1vy+PenBXRqev9wt1c3s7e+nWvPLsTe5UZKeG1LNcPiow2hkp8Ww4PfnYLNGmFMOHvq2shPizHtKKgX+NystZk2s7+DbyRa95IsgZJavW33uj/Ne597395pNJC78OQcKuvbg7Yj0L/7sVmJxqTn3SRt5Tpfv5bd6Wbdjlrjews2oQUqQmrvyunV8eHgWE/a/XWyB/OfHM9h3AMtYNYCVwB3af+/5LX9n0KIFXic/EXAR1JKlxCiRQgxHfgQuBz4S7dzbQAWAOu16LI3gDuFECnafrOBG0P/0Y4dFovgpIz4oM7YvQ3tfoJjdEY8p52URl2Lg+UXTWBPXRstdqep8z/eFsnlp+XTpjU1mpCTxJJnfIXHra9sY9XCqVw4JZckWwSNHU5TYVUyPIm/fW8KWUk2o1vk6kXTqW6yk50UY0SWVRxqDdiy2czh2Runph5Rt6++3WfSBVg8q9AQLuCZ/H/yj09ZvWi6T2TcbfMmsOjpMtNxPfBOOW4Z2Hkvg7zXmw6Ybrdke3Wz6T4RFgzh/O7OWmOfQP6kk0ck8/XRGX6TXqAoLpf7yLE9tVm4fs44n8n6um+MxuWWXDvTU4qnv+20jzWBJu20K6eRnnB0pWxC5WQ/XsK4zQhlmPK/8GgSw4QQVXgiu+4C1gghrgb2AhcDSCm3CSHWAF8AXcA1WgQZwE84Eqb8mvYP4FHgaSFEOR7N5TLtXPVCiNuAj7X9lkkpuwcbDEm8Vf746EifSWNiTiLfPjWfKx//+IgGcl4xI1Kj/ApSerSGHVx2Sh7Pf+JpJbzs/GLTkN2a5k4m5CSSYIvko931phNVUoyVUwvSjDEGWjkG+iFFWI74l47GrLGnro1b1m71M+OclB5vHo3VZGeSpi9bLAJrhDDdT0+WjBDB2x4He697GwH9fb1ky566NnbVtpgmaM4am8GEHE8Ca1yU7/dtds78APcqWH8f78/b2zYLFiHYfqCZP7y1w+d8/WmnfawJ9Ky9X36YR96vGFQa1/EQxh2IkAkYKeW3A7w1K8D+dwB3mGwvA0pMttvRBJTJe48Bj/V6sCHmWNiCu0/c+WkxPkmJi848iV89t8lnxXbry9u495LJ5CTbeOz7p7BR6wOjTywdThc/n1XEvsYOHni3nCtPz6fJ7vKZ5GKsEXxR3cy4rETWlFXxi3NGc+/bO42J5bZ5JWQmRhvjDKbuB/ohleanBm0h3NNEUNNsp7Kug6c3VvqYcYbFR5leLyvJ94cbrPyOzWphQm4Sf7p4Mr981jfRUDdFBbPN620EApVsqWm2884O/wTNZfNKfBJLMxOjjfPo7QgCNbDqTncBEWONYPEzn/ksJnqa0LxX75v2NfhFD5r1kA8nwb7TwWaCOh7CuAMxWJz8xy3HyhZsVn79of+Uc8+CSeyoaaHLLU1XbA6XmyufKOPW84oNO3x2ks2vcdcvzhlNVrKNFc8d8Y8sO7+YOJuFZ8uquOK0fKIiBbZIi0/CXUxUBLnJRzLVg6n700am+f2Qll80kdML0gwTWiDhpPtLzIS0PplUN9mNHBqb1UJmQhTL5pVwi5dj//b5JYYZT8fsB65XBr5oSg65STG8sf2gb2a9dON2SyIjLUYUWXeTIHhqqAUr2ZKZaGPuxGyqmzv4wYwCAJ7/pIpbXtpKaX4KuUkxbD7QxMFmO1PyU7h93nj21NuJEPDIFaVECNGrRUt331Z3k1dfJrT+9JAfKHrS2gaTCWqoJlH2BiVgQsyxcuCZTdyVdR3ERUdwRmEaVovFdMWWEmvlBzMKSIq1Gu+bJSXqJf+9t92y1uODqW6y8+SGSu68cAI/+fsnftcYv/hImZhg6n5PP6Rg+RYVh1v9WsrOHJNp+GC6d2xcPLOIR/+7m6eumsbqRdM52GQ3fESRkb7hpj2Na9O+Bn793Ga/z5SXGsuEnOSgUWI9lWzJS4klJS6aFW9v9ZsI2zqdvLi5wUdALju/hG+OzyAu2nrUk5B3byHvQJLeEug77k0P+YHCLIpOD9qAwWeCGopJlL1BCZgQc6wceGY/6vy0GFLjomh3uBiWEOUXhrv0vGLueeNLdta28ocLSrhhzljuen1HQCexWXXchnYn4Om14nabF7usabb7OM2DqfvBfkiBJq4EWySf72v0ya6/7hujyUpsosXeRWaijW+NzyIlNoqyynpcbk+U0/VzxjEiJY78NGH4XAIRbFx6R83un/tgk50EW/AFRE8lWyrr230CEXRzky7sb+n23i1rt/L3q0+leHhgja63fFnj3xOoN5r1UDHp6N/pyLQ4XFpS09H0vFccPUrAhJjeNhPqabLo/qPOT4vhZzOLuHTVRuNH/reFU3jmh9PZ39iBlLDqva/YvL8ZgD+v28WvZo/lngWTkJg7ic2q42YmRrN4ViEjUmJJsFlNj/NOuOuPuh9o4mp3uPwm6RVv7aS1s8AnTHb2uExyU2KobbFz0ZScY2ZmyE6KMXXCZyfZelxABCvZMmpYXMAIstGZCdS0dJoLtmZ7r8yuXV1utlU3+ZjudO2tv5p1oE6ag5VQ9LxX9IwSMCFmZFoc93/nZD/zjr566q2PxjtjvLrJzrD4aL6nFXYEzwTxo6c/NYoPLnr6U59xzJ2Yw6+f28QPZhSw8atDrPz2ZGyREdS3OUmNs9LldtNq7zIEiB5EcKilEwGseGsnUZGCW88vZunaI87oxTOLcGrxrt0F5bSRaX4TfLBJL5Bw8g7R1bE73eQkx5CdZKO6yc51az7vdW0sM4IJ+XGZCVxzdpGPqWrpecU0dDjITY4LuoAItsDQI8g8pswon3pouck2nC5pbopKiGbhYx8FFQ5dXW5e3LTfJ3rw9vklzJ+UQ2SkhZpmu881weP76Y1m3VNtssHG8ZxnMthRAmYACLZ66u3D73ZL3t1Vawiq8dkJAcKK7aTFRftNTHqtrve+rGXJOZ7qzEvXHhFqy84vYUx2LI9//xSa7U7aOl389B+f+giSpzdW8td3y7l7wSTKa1sYm5XIYx98xdcK09jw1WG6XNKnL/v93zmZUWnx1Ld3EhVhwS0l5bVtPmY870kPzE1Veo2y7hPa/sYOFk7PN2zrR+u07UnIVzV1+Jmqbn15G4vOLCA/Nbi5KJg56cPddawpq+LGOWNpd7p8osKyk2Jo7ehg2fkl3LLW1weTEBPRo9l1W3WTIVz09296cStFGfFMGpFCdpLNrxpAb8v5B/OVDcYJe6iN93hCtUwOMT21Ou1tET3vfvX3ry/nujWbuPy0Iy2N4Yi5qt3R5Vf4clJuMjarhdnFWVgjLIYWol/vlrVbae+UXPnEx2yuajbKwejvr1y/iwun5FJZ18Gew22sXFfOdWs+55qzi1j8zGd8++EP+eHTZVxamkd2ko2U2Ch21bRy1ZMf8fHuBi5dtZF9DXY/f8NNL25l24GmoPcwUI2yZ8uqWLl+F789dxz5aTFH7bQ1y6S/bs3n7D4c/DtySzjYbDc0y4e+N4XVi6Yze1ymof0EK/SYmWijod1BS2eXnwnw9y9vY3xuGg+8u8uoL3f1GQU88O4uWu1u4154f/fenz+Y3wgCl/N3+R5iyrGoTTaQDLXxHk8oDSbE9GSjD2RCyUq0UXGo1TDZNLQ5TCcE7zIturnKGmnxL+3x8R6WnV+CRFLfZp6RX6vZ/AMFAQgt4bAoI55rzi5ECE8ujaNLGvvoGfDgmcCuPqPAiFjr6DRvCbC/qYNJpNAdb7PV+OwEnrxyGu+XH0bKI7k8V59RQLuji9+fV0x2fLTPPeutD0avTN19XHvr2zgpI/B3ZBGQmWDjjS9q/HJkvlWS5SNkzEx3unaz46C5H+Zwa6dp+4K6Vgf3LJjErtoW1pR5EmXvvGCCJ3xa6xmUnRQTNAeoP+X8HS4XN84ZS127wzD7psZGGabSwUagWmqDdbzHE0rAhJienPxmJpT7v3MyX1T7RvjcPt+8OVZRRgLLL5xgFKecU5LF7sOtPrW18tNiuH7OOJwuFxkJMThdbtMxJcdaff42m1B/cc5o2p1dPPpBhc+PVTdT6YJIH5+3sIrtVn1AP++wOP8QWTOz1cMLS3nkfc91zXJ5ls0r4YF3dhkmut5GRXWviqCPKyrSQsWhVvJSYrn7oon8xisMesmsIuKiImjucBrCRf/Mv3z2c8ZknkFhZkLQ6+raTWZCtE+tM/366fH+pk6b1cK26iZjUXHbvBIa2jr54xtf0tDuMD7zuMwEvxygZfNKGJeZCPQvezw9PppOl9svqm9YfOBQ52NZeLKvBKqlNqfk6JqvKXqPMpGFmJ5KdZuZUEalxfuZbG56cQsXl+b6nNtmtVDd2M7ehg521bZwzdlF5CbFkBwTZfygbvjWGBadeRLXrfmcJc9s4oZ/b8bhcnPr+cU+Y1p2fgn/2Lgb8Dh7zXrLxFgjEEh+94J/WO2FU3KNfaU8Ul5F3wbw8HtfsXSu73WXnldMhkkOxp66Npa/vt0wD/1gRgH3rfuSO7Ry+Ga5PLe8tJW5E3OMv69b8zlb9jf2WPo8wRbB0vN8x3X7/Al0Ol289Pl+3t1Vy4jUGFZcPIn7Lp3M3xZOJS4qggf/U0FVY4epJrBbM4H2hpRYK7fPL/G5/q3nF2OxSL/vacksj2lQv87NL22lye4yhLtufq1q6mDNx5XcvWASyy+awN0LJrHm40qqmjqA3pWQD4TL7Qn6SImN4pqzPd9NW2eXX1dNHX2xcO7K9/n2wx/2uV1Af9FrqemtKB79oILr54xTYcoDgNJgQkxfwnb1H2gg88VJ6fHGqtMjFMbT5YYVbx9ZxXsy0KP5ydcL+f3L27j6jAL+/PaRibiyroPbXvmCOy8o4bHvn0JdayfpCTaa2u1MG5XOe7vqqW6ys7psL3/9zhSa7V1UHG7l/vXlNLQ7WH7RxKDms+UXTSQ/NQaLEBRlJHCo1c4fLpjAjS9sYfP+ZqI+3cvDC0s52OwpWdLucJret7q2Tq46fZSPGea8iTl0dDr528KpHAzgYxDC9+91O2rZU9fO8GQbaXHmRQ6bO1y8s6Oa+78zhc1Vjbjc8Jf1O1l05km8+Pl+0uKiaLW7uOHfvomcUZGC1ADlaGzWI/b9QKt3by0tJTaKRWcWkJcaS1p8FE/9dw+zxmUQb7Ma4cBjMxO449Xtflps98+sF+Esq2yirPIzn8+qm2b7E05e2+KJQOuuQeanxfnUQ9M/9566Nr482Gxo4L2J4jqWGs/xnCk/2FECZgAIlsTndkvWf1njE8Z8WkGa6aRV19rpKTni6GJEagwtHS6WrPatgHz985t56sppPPfJXu5eMAm3SQmZyroOqurt7G/q4NRRqXy+r4En/1fJlafnG5PZmMwEbn1lG0k2K9fMLGLy/GTaHC6GJ9nIT4uhsq7DZ2wzCodx4ck5pv1Plp5XzH2XnYyjy01NcwdbDzTR5nAhJfz70yruvTSOkcN8701MZATtTpePGWbJrCIK0uPZXt1sGlmWnxbD6MwEo8Lvy5v2k2iL5PrnNxul783MZo4uF/NPzuNar745ALe98gVXn1HA4TYHd77mX3tr1cJSkmMiue4bo1nx1pH6bNd9YzQpmrkxWISaHgCihwu7Jeyrb6e+tZMLp44gOdbKD586UuX52pmFNLQ7fO6TrjF6/50ebzMEfjAT2NFmj2fER3Nxqb8G+dsXtjB5RLJRjqb75+5uSg0U9Rfons0el8nehvajEjpDJVM+nKbEUKAETJjxjg7Tf0xx0ZH8+dLJ/Hz1kR/YnRdMoKXDaZQcWTzLM4mareLbHF3MGpfFb7S8F7OJZm+Dx3l87cxCHnm/whOimhzD4n99DhypV7a6bC9f1bb6rFSXnlfMQ/8pN3wdf7p4MqeMTA1YT+xWTZN69IMK0wZlZnb/JrvTNKhhxcWTaO108egHX/kU+8xPi+Gaswp9+9mcV0xWUrSPL8h75az/mKMiI9hR2RBQIwpUrt8aITgpLYFdtW0+SYdZSTZGp3v8L7sPHzH16ZrG8te3Mzojnr0N7aaawM1zx1Pd2E6X2+aTqxJj9XS61AtNen8X+r3UC2nm9RA+3R86ulzkpcYGDV4xi570boEQzN8TKPJy1cJSo61CX3xsQ4XjsfGYEjBhpqa50zRL/amrphlJkxkJNtodXSx4aIOxn26+NhMenuZkcM+CSaadHPWVpL76NSbvSyYZ56tusvP0xkp+d+440yrNupkqLiqS4cnRxmS9s6Yl4EStX8c78m3FJZOxCPw6I7bYu0wTAaXwaCZXnT4KZ5fLx3xkNs6HvjfVZ5Vvd7rZWdOCRWAEUvz0rELc0vxeSgmRFvP3MhNtVDV18Kc3vzSy/N0S/vTml5QMT6IgPZ7qpnbTZmYVdW1s299kqgnc9soX3L1gEgcb2/1yVa77xmie+eF07F0uBII7/u8L49pSwlMbKo1CmqEyC1U1dFDTbA+qIQWKntQ1q2DCLtCxZZX1fkLneEqWPB4TQpWACTPNdvOQ4aYOJ9NGpRkP1oavDvvt9/Km/X7hl3dcMIH81FiqGtuMc6XER/H0VdOoaujAGmGhqrGdS0pzSY2N4qH3Kox2wa2dLu69ZDJ3vb6dyroOwxxjNr5PKht8Iplqmjv5+erP+cGMAtOyKmM009W2qkZOK0hjdGYC2Yk2Ol1dzLnvfWP8enLmsHj/tsBLZhWREhPJL2ePJSE6gn99tIfvTh9FQ5uTCIt5Txe70xOi+vrWaq45u5AICzhdkje3HTR8VxEWePlT/3t589zxrHrvK26eO54/XTyZx//7FZefXkCHo4u81Fhyk2LYdKCRhdNHcs+bXxrH/Wr2GKM3SlRkhE8EE3gimG6bV8Kasip+OXu06bhdLjcWi+AfXi0IAP7xYSUzx2QwOS+FikOt7KxtNcoBge8k35NZ6GjNMdlJMfzhte1+9+sPF0wwhEagKLVTR6XyjXGZPhWnu9NTgzTv+zRYKiIfC47HxmNKwISZpBjz+l7JMVaf/br/6J7/pIrLT8tn3faDhl9meFIMeWkx7G9qp77Nye7DbbglHGjqYFx2Ao0dTpZ7mVd+cc5o0uOjmFOS7SekRqbFeFacmK/eCzOO+Druf2cX8ybnYHe62VbVyE/PKvQpJ7P0vGIefu8rmuxOfnpWIVc+4dsUbXRGPJv3NxvJmdf+8zOuPbuQ+98p9zORPfS9qfzmuU0smzeeWeOy+dHTnurOS2YVmo4zKcbK+h0H/T7jbfNKjOumx0dx47fGIqXgrgsnkhpnJSpSYLUIHv/+NPJSYvm0qo6fnlXE51WNuCX89d1yrj27iLHZ8YZw0cd5z5tfsuZHnt4oLXanqQbT3NFFdZM9oCZgs0YwIjXW9NiGDo/g70/RSTPfn3eF6mAUZyfys5lF/EUzeUVY4OQRyXytYJhxbF5KrF+ju6XnFfPH13ews7Y1qOknUPuEFW996bNfb8OqhwrHY+MxJWDCTFZSNMvOL+YWrwl52fnFZCb5hu52/9E1tDsoSI8jPSHfx++wQmswdrDJ7uPXuXnueJ74326fifDet3dy94JJxvH69t+9sIV7L5nMLzQHdPfmVkvPK+ZPb+4wfDC/OGc0Eo8N6oKpI/zOp/tgAL8KAre+vI27F0xi8b8+8+k1b+8yr9z86V6Pr2RESpwhqADWlPk34Vo8s4gOZxc3/79iLn14o891b35pK/deMpllr3zByPQ4Kmrb+f3LR76D359XzDitBfCew63srbP7dQW9/51d/O7ccabjrGnqhFxItFn9TGAr1+/i8e+fAsDfN+7llrnjWfbKFz7nvuv17dw2r8T02L9ffSrgX5+uey+aYJj5/pbMKqIwPd4v4KI7kZEW5k/KoSgjPmAbhL0N7YYAEgJskRZaOhx899R89jV2BG2xbBb1lZcSizXCMugrOPeHoVKlui8oARNmGlqdPPBuuU8S2APvljMm82TyUo/s1/1Hlx5vo7q5gyXP+NtsH//+KX5+ndte+YI/LpjElzUtPP9JlRHJI6V5o7LtWnZ5dZOdpzZUsujMAkqGJ5EcY+XXz28yoshSYqNoc3QxWtNoLASuAkCA9zocXYB/r/lgZpJDrb6VhvVx/m3hVD7e02Ak0904Zxz7GsxzVbYfbObi0lxcLgzhor/3+5e38cSVp1BxqJX9jR1+db10h3VslHmSZky0J0y5od1p6ktq7nB68pia7MRFR/h8/3qkVU2zeTXl+jaHkUPiXZ9ue3Uzh1rtvdJCzHx/963bxZS8lB4FDHiEzKQRKQHbIOhdRh94p9wIGPEOp++pxbKZee94DzU+HsOplYAJM9VeP0RvDjbbmdRtX+8fXcWhVj7aXW86AdW1OUy3f1nTwiPvVxhO/oZ2BxYherR3VzfZWbmunMevnGqULwHzzph3XjDBNIxZSgKGzibaPObACK/39WRP73PfPn8Cz3y0h2vOLiQqwsKSWYWsKasyItIa2h1ERVi4f325pxr03GJ21rSSm2peNsXlhpLhiQHvV12rg+8//jG3zy8xfT/CAjHWCNOWyDGRHgGTHBdp6ktKjrMamkdSTCTXP7/Fb3yBzKdbDzTR5ZYUD0/gQEOHz7gONHSwt76NkcPig/pY2hzmZXvaNWHfX7zNPWZJsUfTYnmohBr3h+PtMyoBE2Z6qhkViJpme8DIp8wE8xIjesTYyvW7WDKriMQYKw+/95XfBHnL3PH87b2vfK5ns1qIEBbcuINOHL99YYufmq+HJkdFCj9z4NLziklLsLL8wgkkx0Xxq9ljuOfNL41kz/u/M4XGdgdRkRGkxERw0ZQ8n971+rkb2h0s07Lfb/jWGBJsVn6q5bXkp8Vw+/wJ3PTiFp8V9OqyvUzNSyYmKsL0fqXGRWF3uokN8P7JI5I50NTBUxsqfTSQpzZUUqhNEBHCYqopPHXVNKOXzxNXlvLnSyfzRXUzbukJirjslDwk+PkxvBcHT181jTaHf65QXauDvNTgIa+BKlTnpR4bc4y3uSdQbbvB1GJZERqUgAkzxdmJfpOIWd/47mQm2kyjyJZfNBEhpN9E3r0feVaSjVirhTPHZBAh4C+Xncz2g57w3UgLXHZKnp/QOdjcSX1bpyGQAk0cVQ3tXH1GAdGRFkqGJ9Lllvx8VhFxtkgEbh6+vJT6VgfJcVZe+rSKhOgs9jV2cKCpg5xkG2sWTaeuzcFn+xq5+cWthoby4HenGMJFv9Z963bxxwWT2HGwhQfeLefiqSPocLr9qhc889Een0z91WV7+fHXC3G6JcOiI/zqdt16fjHRVo9PobrRbhphJpHYIiNoaHf4aKCeEGaPD+1QgKZh1VqJmewkGwca7T4+mJvnjmf1R3vZWdvKbecX89fvTOHTfY0+5jOAugAFUB+5vLTHkNf8VH8n/O3zS8hPje3jE2yOt7nnUGunUUPO9x4NLef18ZYEORAoARNmeuMwNSMvJdYvkmfyiGROH5nG/mY7a8p2cPeCSQhgV22LXz/y3YfbKMxI4P71R5L09GTI3507jvHZCUb3y6qGdprtTnJTYnngnb1cdfooFp1ZQFFGgukquGR4Elc9+TFLZhVRfqjVL8t9f4OdO1/78khkkVfAwJJZRSTYrJTkJNPhdBuh0jarha4ALZu/rGkxJnh7l5voSIvffmWVTZTXtuBye0x1cyfmkJ4QxZqPK5k/eQQOr5wai4DYqAis2uSRnWzjsQ8qWHHxJJwuSXKclX11bcRaI7nnnR38/rxinwCB2+aVGImWgQpWDk+OAeDCKbmGcNE/j15BYPP+ZoRFsP1gs+kEHRtl3hem3eHqMeS1uxNeSvjLeo8P5liZZ7xbFg915/XxmAQ5EIRFwAgh9gAtgAvoklKWCiFSgdXASGAPcImUskHb/0bgam3/xVLKN7TtU4EngBjgVWCJlFIKIaKBp4CpQB1wqZRyzwB9vD7Tk8O0O263ZFt1Eweb7Pxq9lj2N7bTYndx68vbuG3eBE4vSOOqM04yypB4lxjx1ma+PS3POKfd6WZcVgKLziygtbOLW9ZuY+7EHPJSYlj++pdkJ9n4xaxCfvz1Qh76TzlzJ+YgpZub547ntm4RUA3tDu5eMImUbqVO7E5PEum9WsM1u/NIhNkDWkiy7mgelX4kUbCm2Y7TJQMmPNoijxRsHJOZQFq8ue+iyyUNQWSzWnjiymlcc/Zo1u2oNa1m/NRV07BZLViABVPzWP7GDuZOzCHCAuOyE2myOzl/Uo6fcOpwdFFe10pJTjJupKmPBm1O6qk1wqGWTqwW4adBLZlVRJfbbZpzlJkYHbC9tXcipJnvLxQ5F8eD8/p4TIIcCMKpwZwtpTzs9fcNwDop5V1CiBu0v68XQowHLgOKgeHA20KI0VJKF/AgsAjYiEfAzAFewyOMGqSUhUKIy4DlwKUD9cFCSaAaT//+1OPsLqusJzclhjnFWeQsms66HbWMzow3JkDdzNLQ7mDUsDij5bDNaiHCIrBFRmCLtBj5F7+ePcaIdvr7h3v55TdHayXinQxLiOJPb23xK4P+q9ljWfyvz/jLt082nTy7vKropsRGMTbrSE7N859U0Wx3+mT2F6TH09XlZnNVo/lkzZEyKXe+up0Vl0w03S9Cm8/0v7MSo6lusgcsBXOopZNXNQH3h9d3+OWk3Dx3PAXp8fzAS4jq53/48lIAapo6TX00I1JjPdFmVoupILAIWDKriFhrBCdlxGGzRrB60XTaHS4sQvDz1Z8zNjPeL+do2fnFpMRZyU0OrjUMC6BZpZm0TjgWDHXn9fGYBDkQDCYT2TzgLO31k8C7wPXa9meklJ3AbiFEOTBN04ISpZQbAIQQTwHz8QiYecDvtXM9B9wvhBBSBiooPnQIVuPp0Q8qcLmPrELbHS5WrisnyWbFFhnhl7B3z5s7jHpjl52Sx566Np7eWMl9l07m8sc9Pd9zU2P57bfGcrjNQVFGAj96+khByBu+NcbPV7NkVhHRkRayk2xYAkSNxUdHcO3MQt77spZvTcjm1155PEtmFbH7UKthQtOLHL65vYZ2R5fpZH3T/xvHdecU4XBJLpqai6NL8tqWar/9fnFOkSeUWkBeaizDE2NwuaRP9Jr3ONMToilIj6eyro25E3N8MvJjrBba7E4a2538YEaBEfqtfyd2p8eBnZEYbeqjyYiP5rkfn8buw23cfVEJybHR1Lc5SY2z4pJu9tV18OB/Kmhod/D490/BGhFBcXYikVqPmoZ2B+Nzkk06k27jySunkZcavFRMW2eXqRBu6zw2UWTHG8djEuRAEC4BI4E3hRAS+JuUchWQKaWsBpBSVgshMrR9c/BoKDpV2jan9rr7dv2Yfdq5uoQQTUAa4K0xIYRYhEcDIi8vj6FAoJVUhAUjMuqiKZ7boP8oGjqcvLxpP3cvmGT4IXSfzMr1u3jkilKatSz/6ia7T46J3dFFh9PTXOoHMwp8rv3k/yr58ZkFvr4LawSr3ivnlrnj6XC6uPfSydz12nYfH8uOg55wab0Ui/cE+czHe7n+m2MNjWb569vJS4lhx8Fmpo9KNZ2s0xOjqaxrNzL/vaPLvP1OeWmxxEZFIoEIi+CdXbUk2qykxfknk94+vwTdDZYaF02SLcJPg1kyq4g7Xt1OQ7vDp1KwzWphlKYpJMVEsOz8Em5Z69X46/wSbFGC93cd5rO9dcwal81vtIoEuhaSHBNp3JOP9zTw13fLuX1+CfMn5Rg+jYpDrca908v9CAEu6TY6WwbSGqoazaPf8lJjmZzn3130ROd48COFg3AJmK9JKQ9oQuQtIcSOIPuaGWplkO3BjvHd4BFsqwBKS0uHhHYTaCVVmJHAn97c4dNISf9RLH99O5eW5lFe28LKdb42d7vTzUe760m0RRq2/NS4KMO2b42MoN3hKTypX0u/dnWTnZc37+fX3xzHnro2YqMi+fcn+5g5NotfeP0Qb547nha7k3aHi1hrBA9p/g7dma0LjOwkG1edPoodNS2AJy/mB2cUUFHXxqr3KjhpWBy/OGc09759JGjgF+eMxtnl9guX7l5Uc/lFE9hb1+4TWXfz3PFUHm7j8f9V8pOvF/C3hVNpbHNijRQ0tTs43OrpVeOWbopzkrj6yTK/a+jjX7neE81WXttCYUY8eSmeaKy6Vidvbz/gOXe7k+RYK//YuBu7M4PclFjGD080yt3o571l7TZWLZzKhVNyefSDCjq1qgY3vbiVoox4Jo1IYU5xFp/tbcBmtfhVZH7k/Z4d0MOTbKbCuqfw+BOV48GPFA7CImCklAe0/2uFEC8A04AaIUS2pr1kA7Xa7lWAt/s7Fzigbc812e59TJUQIhJIAupD9XkGkkB1moYnRfP496f5PPTGjyIrgfq2TqTE1JntckNjR5cx2YzPivOz7esFI717sSdGR5ASF80VmjlNX9V1N+Hd9soXPPQ9T4b9Q+9V+JiSIryC5S4/Ld+0B0yHlhTY0O4gxmrx0ZhirBZa7OZJg+OyErnv0smkxkfh6HJx/fOf+Y1rxSWTyEmOJj3BxieVDUYeyo+/XkhizJGfR0t7J39bOJUGzYz1+Ae7eWfnYZ8KBXoi6w1zxrLzUAslOck4XG6+dlKGce4IAeeMzcKNoLy2hVHD4k3H3tDuJMICt80roa6t0/CVHWyyM2mE57tt7XSyeGYR9i6Xn4DtyQGdHGM1NZGldKuBpzjCUPcjhYMBFzBCiDjAIqVs0V7PBpYBa4ErgLu0/1/SDlkL/FMIsQKPk78I+EhK6RJCtAghpgMfApcDf/E65gpgA7AAWH88+F+g7ysp7x9FV5ebOy6YwO9e8E841FsNA1TW2/3yK1au38W9l0ymsd1hCIDFswpZ8bZvCZUdWokZb+xOT3XoJFuEz3ab1cLYrERDKxqREutXcv++dbv463emcO3MQjITYwzNyPsc3m0GvLcLoLq5gzZHFwcDmBYtAr49bSQvfLbXqMx814UTeeHTvYxIGUHFoVbc0k1zp+Tnzx4xY916fjHWyCNdSL0TWe96fQdPXOmpNZYQbfXrX6/3cHF0SVZcaj72lFgr00amcrdWHFL/nrw1DIvFwuqyvfz064V9dkDXtJgHH0wekUxBRoLpMQpFXwmHBpMJvCA8S79I4J9SyteFEB8Da4QQVwN7gYsBpJTbhBBrgC+ALuAaLYIM4CccCVN+TfsH8CjwtBYQUI8nCu24oTcrKYfDxeYDTRxstpOdaGPC8CSioiI4rySbtLgoPt3bYCQc6vk04JncRqSYN5Nq7nByq1fOhln0VaDqAl8dagU8Woqeeb/0vGIe++Arrj27kIyEaCIjzEvut2iO5331babvO11un+ZjhsPa2UV2oo2Kw21ER5pHa6XGRvPvT8t9KjPrfhJHl4uXPt/P6ScNM3wo+jWXrt3G498/hevWbDJNZG3u8IzZ3uUy8oD09259eRvXnl3IPW/u5FBzh19S7K3nF+OSbu55Y5dRin/l+l08+N0pPgm4KbFWLjsljwNNHX12QMdGRZqayGKjIgIeo1D0lQEXMFLKCvArs4WUsg6YFeCYO4A7TLaXASUm2+1oAupExOFw8eLmAz6Z6cvmlTB/4nCioiI4syidvNRYaprtzB6fgcPlZuVlU3C6XKTGRSMDCInY6Ei/Cb77fi9v2m9aluXpjZVcNNVTLflv35tKZIQgOdbKaQWT+XRvI799YQs/m2lecn9nTSuPfuAJCjCrc7azpo0kW4RPKLYuxPTouhvnjPXz3yyZVUR9u4PvTh9l4gfZyt8WTuVv71VwUoa5Getwaye3zB3P9oPNfomsNquFDV8dpq3T3HyXkeAJB95Z2862qkZWLZxKU4eTtPhoqurb+PKgb58Xu9NNl0uyt6Hd0FjHZiayv7EDgX8Jnp4c0A6Xy8fcGSEgNTYKZ/emKwpFPxhMYcqKY8TmA02GcAFtwnxpKwXD4ijVWhuPTItjx8EWn94sKy6ZzBSthHN3P8/imUXsb2z3EQDPf+JfIv+yU/JodzhN827GZCaQEhvFlv1N3PPmTmxWC6sWlvJbzWT39417/YSALpyO+Ex8x3XjnLG0dHaRFBPFna9t8bsXeiLjH17fwZJZRUbVg8KMBP7w6nbuu2xywKrFzR0e4ZAcoOhkekI0lYdbibFG+CSyLplVRHx0JBc+uIGHF041PTYryROsAbBhdz3v7Dzs8/6iMwt8xmOzWvi8qomfPfOZ4cAHaO108bsXtpASG8WiMwsoTI9nfHYiBenxQR3Q6fHRfqa7674xmmHxocmDUZyYKAHTTwZjfaJA/oaDzXbj754yk3U/z66aVrZVNxnmH++M8oZ2B5mJ0fz96lNpsTuJiYok0gIVh1vJS431K9J4z5s7uPy0fGyRFq45uxAh4GDTkVL61U12vqxu4rHvn0JdayfD4qN59uO9PkEBFYdatdV+F1UN7di73Nz/Tjk/mFFgOpHHRUUY18pNieWeNz0Jk3/QwotbOp1kJponHepaxuMf7DYJNS7mUEsHERERjM60eUxyDhcWAVlJNqM/jhtMnelpcVG8ungG9W2dFGXEc/3zm30EfVTkkSrX+WkxXD9nHOW1rfxgRoHRS6XF7jT8aXrFa5vVwupF03t8Bl1uT3fM7t0yZ43N7OvjplAERAmYfjBY6xMFqqacqU2YbrfkUEsnP5jhWSV794fp7hgeFh9lrNDtTjery/Zy7yWTcUuJW0JDWycHGjv4jTZB5qfF8PNzRiOl5N5LJrP9YLNP3s1963bxl8tOZvkbnogu706UZ48exqknDeMqL63q1vOLaWh38s7Ow1o4djx1bQ4SoiPp8Io4Myvvf8cFJTi6JPet8w1NXv3RXsMHFBURQWOH0+/YxTOLaNQ6R27YXc/PZ482Qo1TYq00tnXy6+e2+px3eJKNnbVt/PntndwwZxwAVfXtxFoj/HKF2p0uinOSKUiPZ4pbMiEnySdoA+DVxTPYU9dKdVOnnzZZ39ZJfYA2AzXNnT0+I/XtnabdMuvbOzmJ/kdJDcaFl2LgUQKmHwzW+kTR2sS8tJvj2Ga1BCw1o5uxMhJsdHW5+b+t1Vz//GZSYqP4ydd9kyntXS7+/PZOn+TJlNgoqpvszJ2Yw+7DbUZipl5MU8fudLPlQJNxz9aUVRmVAqYXpBnCRd936dpt/G3hVDbsrufmueOJtgoONDpwuSUnj0g2rlvdZOdprX/9mMx44qIj2VnTYtp47a/fmUKbw8WT/6vg198cS3Sk1SdLXy95s2xeyRHzYH2HUYssLyWGA00dxrX18157dqHhNHdLybUzCynKjGflup2cWpDuSYJ0w2P/283Ky0427kmgoI2C9Hha7E5++g/f8Gq9l0qgci+pcVE9PiMCEbRbZn/oTztmxfGFEjD9oKbZbtqtMNz1ieKjrazbXu2X3HfKyNSApWYWnVnA2KxE8lJi+V9FnWGyqW6y8+B/Kri4NJfJuUnERkVy4wubDUe7HkqsJxwK4RtdZjYBdvcj65UCCgLkhDR3OLn6jAJWf7SXb03I9jM36Rn71U12Hv2ggocXlvL0xgq+/7WTuOvCiT55K3anm0/3NRqN1yItgtjoCK45q9CvbbUF6blu2V7uuXgSt8wtZlNVI/saO3h5034WTs83TIcXTsklPT6aa2cW8vKm/UgJ96/3mKyWzi3moffKDYG8eGZRQGd695V/W6fL9J60O1zUtzn8zG/e7auDUd8WuFtmf+lPO2bF8YUSMP0gO8lm2q0wK8x9LkamxTH/5DyfsFs9qujD3XWmE8vJI5L5+ugM9tS1UVbp2ylTt+//64en0tTh9Ini0o8XXgtTvbaXmdnq9+cV8+B/jmg1F07JNZz6qXGBnelf1rTywzNPMuqW6dftnrG/ZFYRwuJm1rhsP1NbSqyV3LQ4xmUn8sjlpTy1oYIp+YkcbnXztolAPm/SCCMCbW99h2n+0OWn5SMlPp9x6dxi6ts7jTHe+so27l4wiZ01LYZ2NKcky/iMulCpa+vkQKPdxx/z8MJS84i+qAiufGIT3y7N4Ykrp3FIM681dtiJigje6gEgM9G80Z3ex6Y/9Lcds+L4QQmYfuByY/pDmj0+q4cjQ0uwZMxApWbyUj3vB+2UmWgjLkAPet3y8fKm/fx81mhjZf30xkpPdFNGPMPiorjrNd+qxDFe53r8g92mpr2q+jbuX1/O4lnmCYXjshL5y7cnkxxjpbqpA6slkqVrP/X5Xpau3cYTV57Chq/qWPbyFzS0O7htXgnxUVHsOtTKm18c5s0vfErVcd6kEYbZrLKuzcdnpRcYzU2J9RN6ukDxHmN5bYuh0Sy/aCJ1bR4BlJcSy5vba7huzedGSLX3uW56aQvLL5roFwTgcLk5tziTccOT+b5XJYVl55cwMq1nM9TRNrrrDaFux6wYOigB0w9qW8xNZIda7ZyUEd6VWiC7vlmpmSWzithd18qoYXFkJtrY+NUhvyZat88robXTSUeXyy+p8Vezx3DyiGTGZyeyZX8zj/93N9+akM0fF0yivbOLgvQ4puansvVAEztrWzmk+UoSbBE+Tcv0UF09JyQpxkqHw8mu2naPPyNAg7PtB5sNDWbp3GJqW8yj6GqaO5HAdd8YTW2Lnfvf2cXdF01iz+E20/NW1rXxyub9XHN2ETvKa41M/xWXTOLZj/cSY7UQaTFPDj3Q6JurM2tsBqcVpOF0SW5+aYthLnvkilLjuzDrDVNZ10FanJVXuy0W9tS1cfEpeUaYuX7dW9Zu5amrpvX4fBxto7veEOp2zIqhgxIw/WCwmsiCYbEIxmcnmCYlvrp4BiPT4vjl7LHsqmnxcey3O7o41NLJNf/8zMi5yEuJ5WCznac37mHm2GmkxEXx89WeyVJPErRZLby6eAbgEci6ZvPAOx6NZNkr23zMaBt213P+5BxGpMbgcku2V9uNKsn5aTF+Gs7S84r514ceP0hKbBTVzR18LX1YwLBjb01g8cwimjqcrCmr8ks69CRXdvHL2WN5ZdM+k0z/YsZmJ9Ch/d39Wrlax0pd45iQk8yeujbOXfm+j0DY39Dhd2z3v60Rnux672JHI9Pi2HbAvCxPbUvPUWTQ90Z3vWXUMPPKw6OGKQFzoqEETD8YrCayntB9Kt3RgxPcUnLnazv8JroVF0/yy7lYdGaBTwXnQCXN99S1ca0mnHSzU06yJytfj/7SI7hGpMZQOjKNj3fX8c+PfHM1ni3b6+PP0LtrHmp1GBWFt+xr9BNEy84v5vH/+rYGWLl+F09dNY2oSIG9y79eWFpcNJv3NwXI9Pf0Xalpbmfp3GJufcVL6M0tJjUukmcWnepjnjRrtRDrZXI081ktmVWEW0pDMHmHwgfM3wlzsqSqPKzQUQKmHwQyxQwGE5l3NFJ2kg23W1LdbKfF3kVGQjSl+UmUVTYZ+3tW+B7NqzVAeRO3hL98+2Qefu8rNu/3rJ4n5CRx9ugMY/IINLHok2t1k90I5b1WKw3jvc1mtRj9bDqcXX65Gr84ZzTRmhlHCHB0SYTwBAvo+/ma2rpIionkYHOHn4/F7nQb+TBb9tbx5JXTqG3xRG9t2FVDdtIwxmQm0NDmDKgpZCXFcvfr27l7wSQ6HF3ERkXy5P8quH7OOE4ZleZzjJn/q7qx3dDqqpvsRp7RrtpWutxuRg2L44Z/b/YRbnoofJfL31y59Lxiutwuwo2qPKwAJWD6xWDtcued65ISG8U1Z59Ei73Lx5R327wSoJKyyiZsVgv3f+dkpIQNXx0mKcZqWvNr+8EWHv2ggqVzi+GjSnbWtrJlfxNOlzSSS/UyNOAJ4waPIzs2KoLffmsM2cmx7D7chsPlZuNXh0wd2PrxNmukX67GvW/vZNGZBYbDXG+F3Nzp8vOHXf/8Fq48PZ999dDZ5TL9rpJjooiJguyUeJ+2A8vOLyYlNoIb/72FewNUPB4WH0Vrp5PzJuZQXttimNfOm5hDm4lD28z/VaAtRHRzZFxUBMmxVk4ZmUJmoo26tk7TqL3aFjtJsVFENHRwz4JJtDm6iIuKpN3hJCmm5zwYhWIgUAKmHwzWLne7Dx/JdblwSi61LZ0+fWDsTjc3v7SVf1x9Kk63m6xEG19Ut/D//vK+z0r4of/45m7oNcEeeq+c2+aVUNPcSW2L3ShdUpAe75fImZ8WY1RrvrQ0z6c98u3zS/jW+CyfLPa8lFhD89KrB3ija1L66/vWecxcLrebuKgIP3/YuOGJ3PTiVu66cAKZiTaWadWgdW2ozeFEYDVyYPTz3rJ2G09dOY3qJjtvbztgWirm2Y/38t3pI/nS6Z/zkRDt31clkOkIoGBYPLUtdrISbUbba/DUDAu0iHG7Jbes/cLvvVeuPePYPUwKRT9QAqafREUKH2d4VGT47MxdXW62VTdxoPFIdFteSgyx0ZFG1rmO3emmpsXOuROGU3Go1S/58taXt3HPgkm43JKdta1GqZfsJBs/OKOAMq8GWj86s4D6tk4K0uN9hFt2ko1fzh7Lb57bxNVnFPhpIze9uJWTR6RwUka8qXBa86PpppOrt7Pb7nRTWdfO8GSbqT/s6aumMXdiDtGREXS53H7NylJioqhqDBB11tLJtTMLkRJ2HmzgqSunUdNiZ1h8NNYIyS1ra5g3JZfP9tb5NCP7+8bdFA9PNP2OgmXtj0yLMy09dP93Tubaf37mt4gJlNN0uK2TQlRPF0X4UQKmH+iO6+4T4KsDVCrG288yPNnG5qomdtW2ckp+iml0W/ce9SmxUXy8py5gR8ioSAsOl5tJI5JIiY2ksaOLSTlJVDd1+K3YY7Q+IpVaz5bsJBsLp+dTXtsSMATX7nSzt77N8Fd1rzLQ7nD6OdD1z6Fjs1qwWSPYUmUeUbW/sYMH3inn7DHDePS/u4220G4Jj/53N/deMjmgszwzMZqf/eszY9vorGT2NnSwq7aVqfnJrLhkMpEW/CLMbj2/mGhr3xcagUoP/d/PZviFKQfLaQq3iVah0FECph+YRQXp9vFQC5ju9Z6mj0qloc3TbXLcJZNNV/PdM9531bRQ0+IgITrCdKLaeqCJZ8uq/ITV3ReVsGrhVOq9VuwTcjwJenoipu5016sc6+fsfo3YqCOPYPf7GWO18tB7XxhRZLZIT3Xk7qXxD7fY6XB2mZ4/JS4Km9VCU4fTtLhjQ4eDjHirXz+VZecX49092Ga1MCYrgby0WB/TVlllvRGtpt/rpWu38XQvclG6E+h5OtRqZ3rBsF7lNA0GE61CoaMETD8I5Qqyp2q03es9PaJNtucWZ5IcazWtwZWTFGOYfJ7a4GkAJgRIMApO6mavtLgoHvxPBRdOyfURVqeNSqXDKflNtxV7RITU7kk0S2YV0eF0+YTeri7baxqC612apPv9rG2xU1nX4dN1MTvJxj0LJrGjpgWL8DjFx2QnECkEuSmx3OzVZO3W84uxRniqEtc0202LOz7+/VNo7HBT3dBqRJFlJNjYWF5DtkkuS/dQ29oW85peNb3MRfGmr8+TCgdWDHaUgOkHoVpB9qYNgFm9p8/21nHOuGyfJmK3nl8MeErO7204Mlnnp8UwNjOBts4ukmKtHPIKBNAd4ADRkb4T3vfP8M8JWbp2m5E9npcaR1FmvHEevcrxhVNyibR4Qoc/39dEl9tNUWa8T3Z39/uZZNLoq6Hdgf6Xyw0P/qeC3583npTYKO5/Z5dPPs1f3y3njxdNoiA9nopDraaCoK2zi/SEaKKionyiyJbMKiLR5p/L0p3sAELhaJJtj+Z5UuHAisGMEjD9IFQryN60ATCr92SWELh07TZWLZzK7OJsHnrviHD58dcL+ZVXRJd3yX09HPjqMwoYNcy37EewnBD9nswck8ne+jbuvGACv31hi1Hl+BfnjGZHdTPDk22MzoinpJtG0P1+Jtoi/RIml8wq4g+vbvfxJWUl2ahvc/hpO/p9AhiVZl6+ZGRanBbi69uzJU7zKU0vGBb0u5owPIll80r82lNPHN73ml5KI1EcbygB009CsYLsjW/HrN5ToMm/qcPJvz6qNBzcpxWkcvWTZX4+Gr3kvr4twgL7G9t9TFuBKh57r9gtFsHIYR7tJCc5hg931zNyWBwHGttptrt4/H87uffSSaYTp/f97OryRLqtWjiVhnYnWYk2Kg61+Phg9AKNlfXtpuPSNaRR6fH86eLJ/PLZI9rBny6eTEF6PG9vP2iYA/WeLQ/+p8LQ/oIRFRXB/InDKRgWZ5gzJw5PIkoTUH1FaSSK4wklYAYhvbHFm9V7yk4yPy4xxsrm/c1s3t+MzWphUm6SqSDyLrlvs1o4JT8Vt5TcsnarYXo62NThX4YlwIrdYhGkJ0Tz13fLj8pPFRlpYcZJGWyr9jQo63K7EWA0DMtIiObk3GQiIy091r+yWATfKsliXLZZNFYMDe0OH+1HjyLrDVFREZSOTO3VvgrFiYSQ3kkFJzClpaWyrKws3MMAet+KWQ8E0CfM4Qk21m6t9jXXnF/C29sP8OYXh43zjMlMMJIqdfS6YnqU2YpLJjM+O4HDrZ3s79aj5LHvTyUqIrJXK/Zj2Va6++ftbj7q6f1AdHW5eXHTfr/S9fMn5RyT6sIKxfGMEOITKWWp6XtKwHgYTAIGjn6ydDhcbD7QZEz+JVmJHGix+2WOm03647MTONjsf72jHUt/P8tAoiepHuvS9QrF8c4JK2CEEHOA+4AI4BEp5V2B9h1sAibUDIVJX6FQDH6CCZjj1gcjhIgAHgC+AVQBHwsh1kopvwjvyAYHypmsUChCzfFsA5gGlEspK6SUDuAZYF6Yx6RQKBQnDMezgMkB9nn9XaVtMxBCLBJClAkhyg4dOjSgg1MoFIrjneNZwJg5FHwcTlLKVVLKUillaXp6+gANS6FQKE4MjmcBUwV4dxvPBQ6EaSwKhUJxwnE8C5iPgSIhxCghRBRwGbA2zGNSKBSKE4bjPUz5XODPeMKUH5NS3hFk30NAG3A40D5hZBhqXH1BjatvqHH1DTUuX/KllKY+huNawPQVIURZoHjucKLG1TfUuPqGGlffUOPqPceziUyhUCgUYUQJGIVCoVCEBCVgfFkV7gEEQI2rb6hx9Q01rr6hxtVLlA9GoVAoFCFBaTAKhUKhCAlKwCgUCoUiJJyQAkYIMUcI8aUQolwIcYPJ+2cJIZqEEJ9r/24ZgDE9JoSoFUJsDfC+EEKs1Ma8WQgxJdRj6uW4BvxeadcdIYR4RwixXQixTQixxGSfAb9nvRxXOJ4vmxDiIyHEJm1ct5rsE4771ZtxhesZixBCfCaEeMXkvbD8HnsxrrDcq4BIKU+of3iSLr8CCoAoYBMwvts+ZwGvDPC4zgSmAFsDvH8u8BqeGmvTgQ8HybgG/F5p180GpmivE4CdJt/jgN+zXo4rHM+XAOK111bgQ2D6ILhfvRlXuJ6x64B/ml07XL/HXowrLPcq0L8TUYMZlGX8pZTvAfVBdpkHPCU9bASShRDZg2BcYUFKWS2l/FR73QJsp1u1bMJwz3o5rgFHuwet2p9W7V/3CJ9w3K/ejGvAEULkAv8PeCTALmH5PfZiXIOKE1HA9FjGX+M0TW1/TQhRPDBDC0pvxx0OwnqvhBAjgZPxrH69Ces9CzIuCMM900wrnwO1wFtSykFxv3oxLhj4+/Vn4DeAO8D74Xq2/kzwccEgmrtORAHTYxl/4FM89XUmAX8BXgz1oHpBb8YdDsJ6r4QQ8cDzwM+llM3d3zY5ZEDuWQ/jCss9k1K6pJST8VQWnyaEKOm2S1juVy/GNaD3SwgxF6iVUn4SbDeTbSG9V70c16Cau05EAdNjGX8pZbOutkspXwWsQohhAzdEUwZl+4Fw3ishhBXPJP4PKeW/TXYJyz3raVzhfr6klI3Au8Ccbm+F9RkLNK4w3K+vAecLIfbgMaHPFEL8vds+4bhXPY4r3M9Wd05EAdNjGX8hRJYQQmivp+G5T3UDPlJf1gKXa9Er04EmKWV1mMcUtnulXfNRYLuUckWA3Qb8nvVmXOG4Z0KIdCFEsvY6BjgH2NFtt3Dcrx7HNdD3S0p5o5QyV0o5Es/8sF5K+b1uuw34verNuAbb3BUZrguHCylllxDiWuANjpTx3yaE+LH2/kPAAuAnQoguoAO4TEoZavX3X3giQIYJIaqApXgcnvqYXsUTuVIOtANXhnI8fRjXgN8rja8BC4Etmv0e4LdAntfYwnHPejOucNyzbOBJIUQEnklnjZTylW7PfTjuV2/GFa5nzIdBcK96M65Bca+MsYXx2gqFQqE4jjkRTWQKhUKhGACUgFEoFApFSFACRqFQKBQhQQkYhUKhUIQEJWAUCoVCERKUgFEoQowQ4l4hxM+9/n5DCPGI199/EkJcF+DYZUKIc3o4/++FEL8y2Z4shPhpP4auUPQLJWAUitDzP+B0ACGEBRgGeNeIOh34r9mBUspbpJRvH+V1kwElYBRhQwkYhSL0/BdNwOARLFuBFiFEihAiGhgHIIT4jxDiE03Dyda2PSGEWKC9PlcIsUMI8YHw9CLx7gcyXgjxrhCiQgixWNt2F3CS8PQF+eNAfFCFwpsTLpNfoRhopJQHhBBdQog8PIJmA57Ku6cBTXhK+t8LzJNSHhJCXArcAVyln0MIYQP+BpwppdytVVjwZixwNp4eNF8KIR4EbgBKtEKSCsWAowSMQjEw6FrM6cAKPALmdDwCZj8wG3hLKyMVAXSvazUWqJBS7tb+/hewyOv9/5NSdgKdQohaIDNEn0Oh6DVKwCgUA4Puh5mAx0S2D/gl0AysB3KklKcFOd6sPLw3nV6vXajftmIQoHwwCsXA8F9gLlCv9T+px+OEPw1YDaQLIU4DT7l/4d8oagdQIDxNzAAu7cU1W/CYzBSKsKAEjEIxMGzBEz22sdu2JillLZ4quMuFEJuAzzkSFACAlLIDT0TY60KID4AaPOa1gEgp64D/CiG2Kie/IhyoasoKxRBBCBEvpWzV+n08AOySUt4b7nEpFIFQGoxCMXT4odZjZhuQhCeqTKEYtCgNRqFQKBQhQWkwCoVCoQgJSsAoFAqFIiQoAaNQKBSKkKAEjEKhUChCghIwCoVCoQgJ/x/x2JbSQ4kZdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Weight'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25cda7d7",
   "metadata": {},
   "source": [
    "# Model creating and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "42cfb273",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:07.689728Z",
     "iopub.status.busy": "2022-11-16T06:01:07.689310Z",
     "iopub.status.idle": "2022-11-16T06:01:07.700446Z",
     "shell.execute_reply": "2022-11-16T06:01:07.698813Z"
    },
    "papermill": {
     "duration": 0.054407,
     "end_time": "2022-11-16T06:01:07.703233",
     "exception": false,
     "start_time": "2022-11-16T06:01:07.648826",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram            0.742905\n",
       "Weight         0.209867\n",
       "Price          1.000000\n",
       "Touchscreen    0.192917\n",
       "Ips            0.253320\n",
       "ppi            0.475368\n",
       "HDD           -0.096891\n",
       "SSD            0.670660\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the attribute co-relation\n",
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31a409a7",
   "metadata": {},
   "source": [
    "Co-relation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0e3ec750",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:07.796278Z",
     "iopub.status.busy": "2022-11-16T06:01:07.795790Z",
     "iopub.status.idle": "2022-11-16T06:01:08.068472Z",
     "shell.execute_reply": "2022-11-16T06:01:08.067299Z"
    },
    "papermill": {
     "duration": 0.320917,
     "end_time": "2022-11-16T06:01:08.070840",
     "exception": false,
     "start_time": "2022-11-16T06:01:07.749923",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019416da",
   "metadata": {},
   "source": [
    "Checking the distribution of the `Price` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "eacf55d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:08.150109Z",
     "iopub.status.busy": "2022-11-16T06:01:08.149451Z",
     "iopub.status.idle": "2022-11-16T06:01:08.402159Z",
     "shell.execute_reply": "2022-11-16T06:01:08.400967Z"
    },
    "papermill": {
     "duration": 0.29506,
     "end_time": "2022-11-16T06:01:08.404504",
     "exception": false,
     "start_time": "2022-11-16T06:01:08.109444",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiva\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(df['Price']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c016bcfe",
   "metadata": {},
   "source": [
    "We can see that it is normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "9c14528b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:08.484739Z",
     "iopub.status.busy": "2022-11-16T06:01:08.484309Z",
     "iopub.status.idle": "2022-11-16T06:01:08.491131Z",
     "shell.execute_reply": "2022-11-16T06:01:08.489964Z"
    },
    "papermill": {
     "duration": 0.049445,
     "end_time": "2022-11-16T06:01:08.493415",
     "exception": false,
     "start_time": "2022-11-16T06:01:08.443970",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
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       "      <td>1.30</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
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       "      <td>0</td>\n",
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       "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "0      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "1      Apple           Ultrabook    8    1.34            0    0  127.677940   \n",
       "2         HP            Notebook    8    1.86            0    0  141.211998   \n",
       "3      Apple           Ultrabook   16    1.83            0    1  220.534624   \n",
       "4      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  157.350512   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  276.053530   \n",
       "1300  Lenovo            Notebook    2    1.50            0    0  111.935204   \n",
       "1301      HP            Notebook    6    2.19            0    0  100.454670   \n",
       "1302    Asus            Notebook    4    2.20            0    0  100.454670   \n",
       "\n",
       "                  Cpu brand   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  Other Intel Processor     0    0     Intel             Windows  \n",
       "1301          Intel Core i7  1000    0       AMD             Windows  \n",
       "1302  Other Intel Processor   500    0     Intel             Windows  \n",
       "\n",
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    "X"
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    {
     "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",
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       "Name: Price, Length: 1302, dtype: float64"
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   "source": [
    "y"
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  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "ec739863",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:08.773734Z",
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    },
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   },
   "outputs": [],
   "source": [
    "# Splitting the dataset\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=2)"
   ]
  },
  {
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   "id": "a918105a",
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     "status": "completed"
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   },
   "outputs": [
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Apple</td>\n",
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       "      Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "183   Toshiba            Notebook    8    2.00            0    0  100.454670   \n",
       "1141      MSI              Gaming    8    2.40            0    0  141.211998   \n",
       "1049     Asus             Netbook    4    1.20            0    0  135.094211   \n",
       "1020     Dell  2 in 1 Convertible    4    2.08            1    1  141.211998   \n",
       "878      Dell            Notebook    4    2.18            0    0  141.211998   \n",
       "...       ...                 ...  ...     ...          ...  ...         ...   \n",
       "466      Acer            Notebook    4    2.20            0    0  100.454670   \n",
       "299      Asus           Ultrabook   16    1.63            0    0  141.211998   \n",
       "493      Acer            Notebook    8    2.20            0    0  100.454670   \n",
       "527    Lenovo            Notebook    8    2.20            0    0  100.454670   \n",
       "1193    Apple           Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu brand   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  Other Intel Processor     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 Processor  1000    0       AMD             Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669a37f9",
   "metadata": {
    "papermill": {
     "duration": 0.042618,
     "end_time": "2022-11-16T06:01:09.617356",
     "exception": false,
     "start_time": "2022-11-16T06:01:09.574738",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "18584428",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:09.701558Z",
     "iopub.status.busy": "2022-11-16T06:01:09.700644Z",
     "iopub.status.idle": "2022-11-16T06:01:09.740502Z",
     "shell.execute_reply": "2022-11-16T06:01:09.739193Z"
    },
    "papermill": {
     "duration": 0.085225,
     "end_time": "2022-11-16T06:01:09.743705",
     "exception": false,
     "start_time": "2022-11-16T06:01:09.658480",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8073277448418701\n",
      "MAE 0.21017827976428766\n"
     ]
    }
   ],
   "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",
    "r1=r2_score(y_test,y_pred)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f256f48",
   "metadata": {
    "papermill": {
     "duration": 0.055433,
     "end_time": "2022-11-16T06:01:10.347305",
     "exception": false,
     "start_time": "2022-11-16T06:01:10.291872",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "d4e38a65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:10.431593Z",
     "iopub.status.busy": "2022-11-16T06:01:10.431091Z",
     "iopub.status.idle": "2022-11-16T06:01:10.462307Z",
     "shell.execute_reply": "2022-11-16T06:01:10.461086Z"
    },
    "papermill": {
     "duration": 0.076669,
     "end_time": "2022-11-16T06:01:10.465359",
     "exception": false,
     "start_time": "2022-11-16T06:01:10.388690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8021984604448553\n",
      "MAE 0.19319716721521116\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = KNeighborsRegressor(n_neighbors=3)\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",
    "r2=r2_score(y_test,y_pred)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfbe450",
   "metadata": {
    "papermill": {
     "duration": 0.055892,
     "end_time": "2022-11-16T06:01:10.587385",
     "exception": false,
     "start_time": "2022-11-16T06:01:10.531493",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "30264d0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:10.669576Z",
     "iopub.status.busy": "2022-11-16T06:01:10.669144Z",
     "iopub.status.idle": "2022-11-16T06:01:10.694164Z",
     "shell.execute_reply": "2022-11-16T06:01:10.692991Z"
    },
    "papermill": {
     "duration": 0.070282,
     "end_time": "2022-11-16T06:01:10.697981",
     "exception": false,
     "start_time": "2022-11-16T06:01:10.627699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8309676535952164\n",
      "MAE 0.18653165877433367\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = DecisionTreeRegressor(max_depth=8)\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",
    "r3=r2_score(y_test,y_pred)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f27a364",
   "metadata": {
    "papermill": {
     "duration": 0.040603,
     "end_time": "2022-11-16T06:01:11.825757",
     "exception": false,
     "start_time": "2022-11-16T06:01:11.785154",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "bd9cc832",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-16T06:01:11.911369Z",
     "iopub.status.busy": "2022-11-16T06:01:11.910652Z",
     "iopub.status.idle": "2022-11-16T06:01:12.218108Z",
     "shell.execute_reply": "2022-11-16T06:01:12.216512Z"
    },
    "papermill": {
     "duration": 0.353116,
     "end_time": "2022-11-16T06:01:12.220637",
     "exception": false,
     "start_time": "2022-11-16T06:01:11.867521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8873402378382488\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.5,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\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",
    "r4=r2_score(y_test,y_pred)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  }
 ],
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