DataWrangling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Required Libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the updated dataset\n",
    "\n",
    "import asyncio\n",
    "import aiohttp\n",
    "\n",
    "async def download(url, filename):\n",
    "    async with aiohttp.ClientSession() as session:\n",
    "        async with session.get(url) as response:\n",
    "            if response.status == 200:\n",
    "                with open(filename, 'wb') as f:\n",
    "                    f.write(await response.read())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-Coursera/laptop_pricing_dataset_mod1.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To obtain the dataset, utilize the download() function as defined above:\n",
    "\n",
    "await download(file_path, \"laptops.csv\")\n",
    "file_name=\"laptops.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we load data into a pandas.DataFrame:\n",
    "\n",
    "df = pd.read_csv(file_name, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 238 entries, 0 to 237\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Unnamed: 0      238 non-null    int64  \n",
      " 1   Manufacturer    238 non-null    object \n",
      " 2   Category        238 non-null    int64  \n",
      " 3   Screen          238 non-null    object \n",
      " 4   GPU             238 non-null    int64  \n",
      " 5   OS              238 non-null    int64  \n",
      " 6   CPU_core        238 non-null    int64  \n",
      " 7   Screen_Size_cm  234 non-null    float64\n",
      " 8   CPU_frequency   238 non-null    float64\n",
      " 9   RAM_GB          238 non-null    int64  \n",
      " 10  Storage_GB_SSD  238 non-null    int64  \n",
      " 11  Weight_kg       233 non-null    float64\n",
      " 12  Price           238 non-null    int64  \n",
      "dtypes: float64(3), int64(8), object(2)\n",
      "memory usage: 24.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Manufacturer</th>\n",
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       "      <th>Screen</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
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       "      <th>Screen_Size_cm</th>\n",
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       "      <th>RAM_GB</th>\n",
       "      <th>Storage_GB_SSD</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Acer</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>2.20</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>2.20</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Dell</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>33.782</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>HP</td>\n",
       "      <td>4</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.91</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Manufacturer  Category     Screen  GPU  OS  CPU_core  \\\n",
       "0           0         Acer         4  IPS Panel    2   1         5   \n",
       "1           1         Dell         3    Full HD    1   1         3   \n",
       "2           2         Dell         3    Full HD    1   1         7   \n",
       "3           3         Dell         4  IPS Panel    2   1         5   \n",
       "4           4           HP         4    Full HD    2   1         7   \n",
       "\n",
       "   Screen_Size_cm  CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_kg  Price  \n",
       "0          35.560            1.6       8             256       1.60    978  \n",
       "1          39.624            2.0       4             256       2.20    634  \n",
       "2          39.624            2.7       8             256       2.20    946  \n",
       "3          33.782            1.6       8             128       1.22   1244  \n",
       "4          39.624            1.8       8             256       1.91    837  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Category</th>\n",
       "      <th>Screen</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>CPU_core</th>\n",
       "      <th>Screen_Size_cm</th>\n",
       "      <th>CPU_frequency</th>\n",
       "      <th>RAM_GB</th>\n",
       "      <th>Storage_GB_SSD</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Acer</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <td>1.6</td>\n",
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       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.62</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>2.20</td>\n",
       "      <td>946</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Dell</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>33.78</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>HP</td>\n",
       "      <td>4</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.62</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.91</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Manufacturer  Category     Screen  GPU  OS  CPU_core  \\\n",
       "0           0         Acer         4  IPS Panel    2   1         5   \n",
       "1           1         Dell         3    Full HD    1   1         3   \n",
       "2           2         Dell         3    Full HD    1   1         7   \n",
       "3           3         Dell         4  IPS Panel    2   1         5   \n",
       "4           4           HP         4    Full HD    2   1         7   \n",
       "\n",
       "   Screen_Size_cm  CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_kg  Price  \n",
       "0           35.56            1.6       8             256       1.60    978  \n",
       "1           39.62            2.0       4             256       2.20    634  \n",
       "2           39.62            2.7       8             256       2.20    946  \n",
       "3           33.78            1.6       8             128       1.22   1244  \n",
       "4           39.62            1.8       8             256       1.91    837  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Screen_Size_cm\"]] = np.round(df[[\"Screen_Size_cm\"]], 2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  Manufacturer  Category  Screen    GPU     OS  CPU_core  \\\n",
      "0       False         False     False   False  False  False     False   \n",
      "1       False         False     False   False  False  False     False   \n",
      "2       False         False     False   False  False  False     False   \n",
      "3       False         False     False   False  False  False     False   \n",
      "4       False         False     False   False  False  False     False   \n",
      "\n",
      "   Screen_Size_cm  CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_kg  Price  \n",
      "0           False          False   False           False      False  False  \n",
      "1           False          False   False           False      False  False  \n",
      "2           False          False   False           False      False  False  \n",
      "3           False          False   False           False      False  False  \n",
      "4           False          False   False           False      False  False  \n",
      "Unnamed: 0\n",
      "Unnamed: 0\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Manufacturer\n",
      "Manufacturer\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Category\n",
      "Category\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Screen\n",
      "Screen\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "GPU\n",
      "GPU\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "OS\n",
      "OS\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "CPU_core\n",
      "CPU_core\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Screen_Size_cm\n",
      "Screen_Size_cm\n",
      "False    234\n",
      "True       4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "CPU_frequency\n",
      "CPU_frequency\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "RAM_GB\n",
      "RAM_GB\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Storage_GB_SSD\n",
      "Storage_GB_SSD\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Weight_kg\n",
      "Weight_kg\n",
      "False    233\n",
      "True       5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Price\n",
      "Price\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the dataset for missing data\n",
    "\n",
    "missing_data = df.isnull()\n",
    "print(missing_data.head())\n",
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print (missing_data[column].value_counts())\n",
    "    print(\"\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_18656\\707358088.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[\"Weight_kg\"].replace(np.nan, avg_weight, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# replacing missing data with mean\n",
    "avg_weight=df['Weight_kg'].astype('float').mean(axis=0)\n",
    "df[\"Weight_kg\"].replace(np.nan, avg_weight, inplace=True)\n",
    "\n",
    "# astype() function converts the values to the desired data type\n",
    "# axis=0 indicates that the mean value is to calculated across all column elements in a row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_18656\\2852884260.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[\"Screen_Size_cm\"].replace(np.nan, common_screen_size, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# replacing missing data with mode\n",
    "common_screen_size = df['Screen_Size_cm'].value_counts().idxmax()\n",
    "df[\"Screen_Size_cm\"].replace(np.nan, common_screen_size, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 238 entries, 0 to 237\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Unnamed: 0      238 non-null    int64  \n",
      " 1   Manufacturer    238 non-null    object \n",
      " 2   Category        238 non-null    int64  \n",
      " 3   Screen          238 non-null    object \n",
      " 4   GPU             238 non-null    int64  \n",
      " 5   OS              238 non-null    int64  \n",
      " 6   CPU_core        238 non-null    int64  \n",
      " 7   Screen_Size_cm  238 non-null    float64\n",
      " 8   CPU_frequency   238 non-null    float64\n",
      " 9   RAM_GB          238 non-null    int64  \n",
      " 10  Storage_GB_SSD  238 non-null    int64  \n",
      " 11  Weight_kg       238 non-null    float64\n",
      " 12  Price           238 non-null    int64  \n",
      "dtypes: float64(3), int64(8), object(2)\n",
      "memory usage: 24.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data standardization: convert weight from kg to pounds\n",
    "df[\"Weight_kg\"] = df[\"Weight_kg\"]*2.205\n",
    "df.rename(columns={'Weight_kg':'Weight_pounds'}, inplace=True)\n",
    "\n",
    "# Data standardization: convert screen size from cm to inch\n",
    "df[\"Screen_Size_cm\"] = df[\"Screen_Size_cm\"]/2.54\n",
    "df.rename(columns={'Screen_Size_cm':'Screen_Size_inch'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Normalization\n",
    "\n",
    "df['CPU_frequency'] = df['CPU_frequency']/df['CPU_frequency'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binning\n",
    "\n",
    "bins = np.linspace(min(df[\"Price\"]), max(df[\"Price\"]), 4)\n",
    "group_names = ['Low', 'Medium', 'High']\n",
    "df['Price-binned'] = pd.cut(df['Price'], bins, labels=group_names, include_lowest=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price bins')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDSXhp2tW7dqwIABCgsLk81m05o1a+x95eXlmjp1qtq0aSNfX1+FhYXpL3/5i44ePeowx/HjxxUXFyc/Pz8FBARo9OjRKi4uvsZnAgAA3JVLw05JSYnatWunlJSU8/pOnTqlvXv3avr06dq7d6/eeust7d+/X/fcc4/DuLi4OH355ZfauHGj1q9fr61bt2rs2LHX6hQAAICbs1mWZbm6CEmy2WxavXq1Bg0adNExu3btUpcuXfTtt98qIiJC+/btU6tWrbRr1y516tRJkrRhwwb169dP3333ncLCwq7otYuKiuTv76/CwkL5+fk543TsGk17x6nzofo5/HR/V5cAAEa60vfvavXMTmFhoWw2mwICAiRJGRkZCggIsAcdSYqOjpaHh4d27NjhoioBAIA78XR1AVeqtLRUU6dO1f33329Pb7m5uapfv77DOE9PTwUGBio3N/eic5WVlamsrMy+X1RUVDVFAwAAl6sWKzvl5eUaOnSoLMvS4sWLr3q+5ORk+fv727fw8HAnVAkAANyR24edX4LOt99+q40bNzrckwsNDVV+fr7D+DNnzuj48eMKDQ296JxJSUkqLCy0bzk5OVVWPwAAcC23vo31S9DJysrS5s2bFRQU5NAfFRWlgoIC7dmzRx07dpQkffjhh6qoqFDXrl0vOq+Xl5e8vLyqtHYAAOAeXBp2iouLdfDgQft+dna2MjMzFRgYqAYNGuhPf/qT9u7dq/Xr1+vs2bP253ACAwNVq1YttWzZUn369NFDDz2k1NRUlZeXKyEhQcOGDbviT2IBAACzuTTs7N69W7169bLvJyYmSpJGjBihJ598Um+//bYkqX379g7Hbd68WT179pQkrVy5UgkJCbrzzjvl4eGh2NhYLViw4JrUDwAA3J9Lw07Pnj11qa/5uZKvAAoMDNSqVaucWRYAADCI2z+gDAAAcDUIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0VwadrZu3aoBAwYoLCxMNptNa9ascei3LEszZsxQgwYN5OPjo+joaGVlZTmMOX78uOLi4uTn56eAgACNHj1axcXF1/AsAACAO3Np2CkpKVG7du2UkpJywf5nn31WCxYsUGpqqnbs2CFfX1/FxMSotLTUPiYuLk5ffvmlNm7cqPXr12vr1q0aO3bstToFAADg5jxd+eJ9+/ZV3759L9hnWZbmz5+vJ554QgMHDpQkrVixQiEhIVqzZo2GDRumffv2acOGDdq1a5c6deokSVq4cKH69eun5557TmFhYdfsXAAAgHty22d2srOzlZubq+joaHubv7+/unbtqoyMDElSRkaGAgIC7EFHkqKjo+Xh4aEdO3Zc85oBAID7cenKzqXk5uZKkkJCQhzaQ0JC7H25ubmqX7++Q7+np6cCAwPtYy6krKxMZWVl9v2ioiJnlQ0AANyM267sVKXk5GT5+/vbt/DwcFeXBAAAqojbhp3Q0FBJUl5enkN7Xl6evS80NFT5+fkO/WfOnNHx48ftYy4kKSlJhYWF9i0nJ8fJ1QMAAHfhtmEnMjJSoaGh2rRpk72tqKhIO3bsUFRUlCQpKipKBQUF2rNnj33Mhx9+qIqKCnXt2vWic3t5ecnPz89hAwAAZnLpMzvFxcU6ePCgfT87O1uZmZkKDAxURESEJk6cqDlz5qhp06aKjIzU9OnTFRYWpkGDBkmSWrZsqT59+uihhx5SamqqysvLlZCQoGHDhvFJLAAAIMnFYWf37t3q1auXfT8xMVGSNGLECKWlpWnKlCkqKSnR2LFjVVBQoG7dumnDhg3y9va2H7Ny5UolJCTozjvvlIeHh2JjY7VgwYJrfi4AAMA92SzLslxdhKsVFRXJ399fhYWFTr+l1WjaO06dD9XP4af7u7oEADDSlb5/u+0zOwAAAM5A2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0SoVdnr37q2CgoLz2ouKitS7d++rrQkAAMBpKhV2tmzZotOnT5/XXlpaqo8++uiqiwIAAHAWz98z+PPPP7f/+auvvlJubq59/+zZs9qwYYNuvPFG51UHAABwlX5X2Gnfvr1sNptsNtsFb1f5+Pho4cKFTisOAADgav2usJOdnS3LsvSHP/xBO3fuVHBwsL2vVq1aql+/vmrUqOH0IgEAACrrd4Wdm266SZJUUVFRJcUAAAA42+8KO7+WlZWlzZs3Kz8//7zwM2PGjKsuDAAAwBkqFXaWLFmicePGqV69egoNDZXNZrP32Ww2wg4AAHAblQo7c+bM0dy5czV16lRn1wMAAOBUlfqenRMnTmjIkCHOrgUAAMDpKhV2hgwZog8++MDZtQAAADhdpW5jNWnSRNOnT9f27dvVpk0b1axZ06F/woQJTikOAADgatksy7J+70GRkZEXn9Bm0zfffHNVRV1rRUVF8vf3V2Fhofz8/Jw6d6Np7zh1PlQ/h5/u7+oSAMBIV/r+XamVnezs7EoXBgAAcC1V6pkdAACA6qJSKzujRo26ZP/SpUsrVQwAAICzVSrsnDhxwmG/vLxcX3zxhQoKCi74A6EAAACuUqmws3r16vPaKioqNG7cODVu3PiqiwIAAHAWpz2z4+HhocTERM2bN89ZUwIAAFw1pz6gfOjQIZ05c8aZUwIAAFyVSt3GSkxMdNi3LEs//PCD3nnnHY0YMcIphQEAADhDpVZ2Pv30U4ft888/lyQ9//zzmj9/vtOKO3v2rKZPn67IyEj5+PiocePG+vvf/65ffw+iZVmaMWOGGjRoIB8fH0VHRysrK8tpNQAAgOqtUis7mzdvdnYdF/TMM89o8eLFWr58uVq3bq3du3frwQcflL+/v/0nKZ599lktWLBAy5cvV2RkpKZPn66YmBh99dVX8vb2viZ1AgAA91WpsPOLY8eOaf/+/ZKk5s2bKzg42ClF/WLbtm0aOHCg+vc/93X7jRo10n/+8x/t3LlT0rlVnfnz5+uJJ57QwIEDJUkrVqxQSEiI1qxZo2HDhjm1HgAAUP1U6jZWSUmJRo0apQYNGqh79+7q3r27wsLCNHr0aJ06dcppxd12223atGmTDhw4IEn67LPP9PHHH6tv376Szv1sRW5urqKjo+3H+Pv7q2vXrsrIyHBaHQAAoPqqVNhJTExUenq61q1bp4KCAhUUFGjt2rVKT0/XY4895rTipk2bpmHDhqlFixaqWbOmOnTooIkTJyouLk6SlJubK0kKCQlxOC4kJMTedyFlZWUqKipy2AAAgJkqdRvrzTff1BtvvKGePXva2/r16ycfHx8NHTpUixcvdkpx//d//6eVK1dq1apVat26tTIzMzVx4kSFhYVd1ae+kpOTNWvWLKfUCAAA3FulVnZOnTp13mqKJNWvX9+pt7EmT55sX91p06aNhg8frkmTJik5OVmSFBoaKknKy8tzOC4vL8/edyFJSUkqLCy0bzk5OU6rGQAAuJdKhZ2oqCjNnDlTpaWl9raff/5Zs2bNUlRUlNOKO3XqlDw8HEusUaOGKioqJEmRkZEKDQ3Vpk2b7P1FRUXasWPHJevw8vKSn5+fwwYAAMxUqdtY8+fPV58+fdSwYUO1a9dO0rmHh728vPTBBx84rbgBAwZo7ty5ioiIUOvWrfXpp5/qhRdesP/qus1m08SJEzVnzhw1bdrU/tHzsLAwDRo0yGl1AACA6qtSYadNmzbKysrSypUr9fXXX0uS7r//fsXFxcnHx8dpxS1cuFDTp0/XX//6V+Xn5yssLEwPP/ywZsyYYR8zZcoUlZSUaOzYsSooKFC3bt20YcMGvmMHAABIkmzWr7+O+AolJycrJCTEvsLyi6VLl+rYsWOaOnWq0wq8FoqKiuTv76/CwkKn39JqNO0dp86H6ufw0/1dXQIAGOlK378r9czOP//5T7Vo0eK89tatWys1NbUyUwIAAFSJSoWd3NxcNWjQ4Lz24OBg/fDDD1ddFAAAgLNUKuyEh4frk08+Oa/9k08+UVhY2FUXBQAA4CyVekD5oYce0sSJE1VeXq7evXtLkjZt2qQpU6Y49RuUAQAArlalws7kyZP1008/6a9//atOnz4tSfL29tbUqVOVlJTk1AIBAACuRqXCjs1m0zPPPKPp06dr37598vHxUdOmTeXl5eXs+gAAAK5KpcLOL2rXrq3OnTs7qxYAAACnq9QDygAAANUFYQcAABiNsAMAAIxG2AEAAEa7qgeUAbg/fp8N/D4brnes7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADCa24ed77//Xg888ICCgoLk4+OjNm3aaPfu3fZ+y7I0Y8YMNWjQQD4+PoqOjlZWVpYLKwYAAO7ErcPOiRMndPvtt6tmzZp677339NVXX+n5559X3bp17WOeffZZLViwQKmpqdqxY4d8fX0VExOj0tJSF1YOAADchaerC7iUZ555RuHh4Vq2bJm9LTIy0v5ny7I0f/58PfHEExo4cKAkacWKFQoJCdGaNWs0bNiwa14zAABwL269svP222+rU6dOGjJkiOrXr68OHTpoyZIl9v7s7Gzl5uYqOjra3ubv76+uXbsqIyPDFSUDAAA349Zh55tvvtHixYvVtGlTvf/++xo3bpwmTJig5cuXS5Jyc3MlSSEhIQ7HhYSE2PsupKysTEVFRQ4bAAAwk1vfxqqoqFCnTp301FNPSZI6dOigL774QqmpqRoxYkSl501OTtasWbOcVSYAAHBjbr2y06BBA7Vq1cqhrWXLljpy5IgkKTQ0VJKUl5fnMCYvL8/edyFJSUkqLCy0bzk5OU6uHAAAuAu3Dju333679u/f79B24MAB3XTTTZLOPawcGhqqTZs22fuLioq0Y8cORUVFXXReLy8v+fn5OWwAAMBMbn0ba9KkSbrtttv01FNPaejQodq5c6deeuklvfTSS5Ikm82miRMnas6cOWratKkiIyM1ffp0hYWFadCgQa4tHgAAuAW3DjudO3fW6tWrlZSUpNmzZysyMlLz589XXFycfcyUKVNUUlKisWPHqqCgQN26ddOGDRvk7e3twsoBAIC7cOuwI0l333237r777ov222w2zZ49W7Nnz76GVQEAgOrCrZ/ZAQAAuFqEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaNUq7Dz99NOy2WyaOHGiva20tFTx8fEKCgpS7dq1FRsbq7y8PNcVCQAA3Eq1CTu7du3SP//5T7Vt29ahfdKkSVq3bp1ef/11paen6+jRoxo8eLCLqgQAAO6mWoSd4uJixcXFacmSJapbt669vbCwUC+//LJeeOEF9e7dWx07dtSyZcu0bds2bd++3YUVAwAAd1Etwk58fLz69++v6Ohoh/Y9e/aovLzcob1FixaKiIhQRkbGtS4TAAC4IU9XF3A5r776qvbu3atdu3ad15ebm6tatWopICDAoT0kJES5ubkXnbOsrExlZWX2/aKiIqfVCwAA3Itbr+zk5OTo0Ucf1cqVK+Xt7e20eZOTk+Xv72/fwsPDnTY3AABwL24ddvbs2aP8/Hzdcsst8vT0lKenp9LT07VgwQJ5enoqJCREp0+fVkFBgcNxeXl5Cg0Nvei8SUlJKiwstG85OTlVfCYAAMBV3Po21p133qn//e9/Dm0PPvigWrRooalTpyo8PFw1a9bUpk2bFBsbK0nav3+/jhw5oqioqIvO6+XlJS8vryqtHQAAuAe3Djt16tTRzTff7NDm6+uroKAge/vo0aOVmJiowMBA+fn5afz48YqKitKtt97qipIBAICbceuwcyXmzZsnDw8PxcbGqqysTDExMVq0aJGrywIAAG6i2oWdLVu2OOx7e3srJSVFKSkprikIAAC4Nbd+QBkAAOBqEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKO5fdhJTk5W586dVadOHdWvX1+DBg3S/v37HcaUlpYqPj5eQUFBql27tmJjY5WXl+eiigEAgDtx+7CTnp6u+Ph4bd++XRs3blR5ebnuuusulZSU2MdMmjRJ69at0+uvv6709HQdPXpUgwcPdmHVAADAXXi6uoDL2bBhg8N+Wlqa6tevrz179qh79+4qLCzUyy+/rFWrVql3796SpGXLlqlly5bavn27br31VleUDQAA3ITbr+z8VmFhoSQpMDBQkrRnzx6Vl5crOjraPqZFixaKiIhQRkaGS2oEAADuw+1Xdn6toqJCEydO1O23366bb75ZkpSbm6tatWopICDAYWxISIhyc3MvOE9ZWZnKysrs+0VFRVVWMwAAcK1qtbITHx+vL774Qq+++upVzZOcnCx/f3/7Fh4e7qQKAQCAu6k2YSchIUHr16/X5s2b1bBhQ3t7aGioTp8+rYKCAofxeXl5Cg0NveBcSUlJKiwstG85OTlVWToAAHAhtw87lmUpISFBq1ev1ocffqjIyEiH/o4dO6pmzZratGmTvW3//v06cuSIoqKiLjinl5eX/Pz8HDYAAGAmt39mJz4+XqtWrdLatWtVp04d+3M4/v7+8vHxkb+/v0aPHq3ExEQFBgbKz89P48ePV1RUFJ/EAgAA7h92Fi9eLEnq2bOnQ/uyZcs0cuRISdK8efPk4eGh2NhYlZWVKSYmRosWLbrGlQIAAHfk9mHHsqzLjvH29lZKSopSUlKuQUUAAKA6cftndgAAAK4GYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADCap6sLAACYrdG0d1xdAlzs8NP9Xfr6rOwAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxmTNhJSUlRo0aN5O3tra5du2rnzp2uLgkAALgBI8LOa6+9psTERM2cOVN79+5Vu3btFBMTo/z8fFeXBgAAXMyIsPPCCy/ooYce0oMPPqhWrVopNTVVN9xwg5YuXerq0gAAgItV+7Bz+vRp7dmzR9HR0fY2Dw8PRUdHKyMjw4WVAQAAd1Dtfxvrxx9/1NmzZxUSEuLQHhISoq+//vqCx5SVlamsrMy+X1hYKEkqKipyen0VZaecPieql6q4rn4PrkFwDcLVquoa/GVey7IuOa7ah53KSE5O1qxZs85rDw8Pd0E1MJ3/fFdXgOsd1yBcraqvwZMnT8rf3/+i/dU+7NSrV081atRQXl6eQ3teXp5CQ0MveExSUpISExPt+xUVFTp+/LiCgoJks9mqtN7rTVFRkcLDw5WTkyM/Pz9Xl4PrENcgXI1rsOpYlqWTJ08qLCzskuOqfdipVauWOnbsqE2bNmnQoEGSzoWXTZs2KSEh4YLHeHl5ycvLy6EtICCgiiu9vvn5+fEfOVyKaxCuxjVYNS61ovOLah92JCkxMVEjRoxQp06d1KVLF82fP18lJSV68MEHXV0aAABwMSPCzn333adjx45pxowZys3NVfv27bVhw4bzHloGAADXHyPCjiQlJCRc9LYVXMfLy0szZ84877YhcK1wDcLVuAZdz2Zd7vNaAAAA1Vi1/1JBAACASyHsAAAAoxF2AACA0Qg7AIy0ZcsW2Ww2FRQUSJLS0tL4Pi1UucpcZyNHjrR/TxyqBmEHvwv/UcJZRo4cKZvNpkceeeS8vvj4eNlsNo0cOdJpr3fffffpwIEDTpsP15+L/f/v18Ga68w9EXYAuEx4eLheffVV/fzzz/a20tJSrVq1ShEREU59LR8fH9WvX9+pcwK/xXXmngg7cJr09HR16dJFXl5eatCggaZNm6YzZ85IktavX6+AgACdPXtWkpSZmSmbzaZp06bZjx8zZoweeOABl9QO17jlllsUHh6ut956y9721ltvKSIiQh06dLC3VVRUKDk5WZGRkfLx8VG7du30xhtvOMz17rvvqlmzZvLx8VGvXr10+PBhh/7f3l640L/SJ06cqJ49e9r3e/bsqfHjx2vixImqW7euQkJCtGTJEvs3tNepU0dNmjTRe++9d9V/FzDDhW5jzZkzR/Xr11edOnU0ZswYTZs2Te3btz/v2Oeee04NGjRQUFCQ4uPjVV5efm2Kvg4QduAU33//vfr166fOnTvrs88+0+LFi/Xyyy9rzpw5kqQ77rhDJ0+e1KeffirpXDCqV6+etmzZYp8jPT3d4Y0G14dRo0Zp2bJl9v2lS5ee91MvycnJWrFihVJTU/Xll19q0qRJeuCBB5Seni5JysnJ0eDBgzVgwABlZmba31CcYfny5apXr5527typ8ePHa9y4cRoyZIhuu+027d27V3fddZeGDx+uU6dOOeX1YJaVK1dq7ty5euaZZ7Rnzx5FRERo8eLF543bvHmzDh06pM2bN2v58uVKS0tTWlratS/YVBbwO4wYMcIaOHDgee1/+9vfrObNm1sVFRX2tpSUFKt27drW2bNnLcuyrFtuucX6xz/+YVmWZQ0aNMiaO3euVatWLevkyZPWd999Z0myDhw4cE3OA673y7WUn59veXl5WYcPH7YOHz5seXt7W8eOHbMGDhxojRgxwiotLbVuuOEGa9u2bQ7Hjx492rr//vsty7KspKQkq1WrVg79U6dOtSRZJ06csCzLspYtW2b5+/uf9/q/9uijj1o9evSw7/fo0cPq1q2bff/MmTOWr6+vNXz4cHvbDz/8YEmyMjIyruJvA9XBiBEjrBo1ali+vr4Om7e3t/1a++111rVrVys+Pt5hnttvv91q166dw7w33XSTdebMGXvbkCFDrPvuu6+qT+m6wcoOnGLfvn2KioqSzWazt91+++0qLi7Wd999J0nq0aOHtmzZIsuy9NFHH2nw4MFq2bKlPv74Y6WnpyssLExNmzZ11SnARYKDg9W/f3+lpaVp2bJl6t+/v+rVq2fvP3jwoE6dOqU//vGPql27tn1bsWKFDh06JOnc9de1a1eHeaOiopxSX9u2be1/rlGjhoKCgtSmTRt72y+/wZefn++U14N769WrlzIzMx22f/3rXxcdv3//fnXp0sWh7bf7ktS6dWvVqFHDvt+gQQOuKScy5rex4P569uyppUuX6rPPPlPNmjXVokUL9ezZU1u2bNGJEyfUo0cPV5cIFxk1apT9t+1SUlIc+oqLiyVJ77zzjm688UaHvqv5rSEPDw9Zv/m1nAs9I1GzZk2HfZvN5tD2S8CvqKiodC2oPnx9fdWkSROHtl/+QXc1LnSdcU05Dys7cIqWLVsqIyPD4c3jk08+UZ06ddSwYUNJ//+5nXnz5tmDzS9hZ8uWLTyvcx3r06ePTp8+rfLycsXExDj0tWrVSl5eXjpy5IiaNGnisIWHh0s6d/3t3LnT4bjt27df8jWDg4P1ww8/OLRlZmZe/ckAv9K8eXPt2rXLoe23+6h6hB38boWFhect444dO1Y5OTkaP368vv76a61du1YzZ85UYmKiPDzOXWZ169ZV27ZttXLlSnuw6d69u/bu3asDBw6wsnMdq1Gjhvbt26evvvrKYSlfkurUqaPHH39ckyZN0vLly3Xo0CHt3btXCxcu1PLlyyVJjzzyiLKysjR58mTt379fq1atuuzDnb1799bu3bu1YsUKZWVlaebMmfriiy+q6hRxnRo/frxefvllLV++XFlZWZozZ44+//xzh1v+qHrcxsLvtmXLFoePBUvS6NGj9e6772ry5Mlq166dAgMDNXr0aD3xxBMO43r06KHMzEx72AkMDFSrVq2Ul5en5s2bX6tTgBvy8/O7aN/f//53BQcHKzk5Wd98840CAgJ0yy236G9/+5skKSIiQm+++aYmTZqkhQsXqkuXLnrqqac0atSoi84ZExOj6dOna8qUKSotLdWoUaP0l7/8Rf/73/+cfm64fsXFxembb77R448/rtLSUg0dOlQjR448byUSVctm/famNQAAqDJ//OMfFRoaqn//+9+uLuW6wcoOAABV5NSpU0pNTVVMTIxq1Kih//znP/rvf/+rjRs3urq06worOwAAVJGff/5ZAwYM0KeffqrS0lI1b95cTzzxhAYPHuzq0q4rhB0AAGA0Po0FAACMRtgBAABGI+wAAACjEXYAAIDRCDsAjNSoUSPNnz/f1WUAcAOEHQBub+TIkbLZbLLZbKpVq5aaNGmi2bNn68yZMxc9ZteuXRo7duw1rBKAu+JLBQFUC3369NGyZctUVlamd999V/Hx8apZs6aSkpIcxp0+fVq1atVScHCwiyoF4G5Y2QFQLXh5eSk0NFQ33XSTxo0bp+joaL399tsaOXKkBg0apLlz5yosLMz+G2u/vY1VUFCghx9+WCEhIfL29tbNN9+s9evX2/s//vhj3XHHHfLx8VF4eLgmTJigkpKSa32aAKoAKzsAqiUfHx/99NNPkqRNmzbJz8/vol/BX1FRob59++rkyZN65ZVX1LhxY4dfWD906JD69OmjOXPmaOnSpTp27JgSEhKUkJCgZcuWXbNzAlA1CDsAqhXLsrRp0ya9//77Gj9+vI4dOyZfX1/961//Uq1atS54zH//+1/t3LlT+/btU7NmzSRJf/jDH+z9ycnJiouL08SJEyVJTZs21YIFC9SjRw8tXrxY3t7eVX5eAKoOt7EAVAvr169X7dq15e3trb59++q+++7Tk08+KUlq06bNRYOOJGVmZqphw4b2oPNbn332mdLS0lS7dm37FhMTo4qKCmVnZ1fF6QC4hljZAVAt9OrVS4sXL1atWrUUFhYmT8///78vX1/fSx7r4+Nzyf7i4mI9/PDDmjBhwnl9ERERlSsYgNsg7ACoFnx9fdWkSZNKHdu2bVt99913OnDgwAVXd2655RZ99dVXlZ4fgHvjNhYA4/Xo0UPdu3dXbGysNm7cqOzsbL333nvasGGDJGnq1Knatm2bEhISlJmZqaysLK1du1YJCQkurhyAMxB2AFwX3nzzTXXu3Fn333+/WrVqpSlTpujs2bOSzq38pKen68CBA7rjjjvUoUMHzZgxQ2FhYS6uGoAz2CzLslxdBAAAQFVhZQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAo/0/ubC6v6TrwykAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the bar graph of these bins\n",
    "\n",
    "plt.bar(group_names, df[\"Price-binned\"].value_counts())\n",
    "plt.xlabel(\"Price\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Price bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Indicator Variable: Screen\n",
    "dummy_variable_1 = pd.get_dummies(df[\"Screen\"])\n",
    "dummy_variable_1.rename(columns={'IPS Panel':'Screen-IPS_panel', 'Full HD':'Screen-Full_HD'}, inplace=True)\n",
    "df = pd.concat([df, dummy_variable_1], axis=1)\n",
    "\n",
    "# drop original column \"Screen\" from \"df\"\n",
    "df.drop(\"Screen\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Category</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>CPU_core</th>\n",
       "      <th>Screen_Size_inch</th>\n",
       "      <th>CPU_frequency</th>\n",
       "      <th>RAM_GB</th>\n",
       "      <th>Storage_GB_SSD</th>\n",
       "      <th>Weight_pounds</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price-binned</th>\n",
       "      <th>Screen-Full_HD</th>\n",
       "      <th>Screen-IPS_panel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Acer</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>3.52800</td>\n",
       "      <td>978</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15.598425</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>4.85100</td>\n",
       "      <td>634</td>\n",
       "      <td>Low</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>15.598425</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4.85100</td>\n",
       "      <td>946</td>\n",
       "      <td>Low</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Dell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13.299213</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>2.69010</td>\n",
       "      <td>1244</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>HP</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>15.598425</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4.21155</td>\n",
       "      <td>837</td>\n",
       "      <td>Low</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Manufacturer  Category  GPU  OS  CPU_core  Screen_Size_inch  \\\n",
       "0           0         Acer         4    2   1         5         14.000000   \n",
       "1           1         Dell         3    1   1         3         15.598425   \n",
       "2           2         Dell         3    1   1         7         15.598425   \n",
       "3           3         Dell         4    2   1         5         13.299213   \n",
       "4           4           HP         4    2   1         7         15.598425   \n",
       "\n",
       "   CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_pounds  Price Price-binned  \\\n",
       "0       0.551724       8             256        3.52800    978          Low   \n",
       "1       0.689655       4             256        4.85100    634          Low   \n",
       "2       0.931034       8             256        4.85100    946          Low   \n",
       "3       0.551724       8             128        2.69010   1244          Low   \n",
       "4       0.620690       8             256        4.21155    837          Low   \n",
       "\n",
       "   Screen-Full_HD  Screen-IPS_panel  \n",
       "0           False              True  \n",
       "1            True             False  \n",
       "2            True             False  \n",
       "3           False              True  \n",
       "4            True             False  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  }
 },
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}