Laptop_Pricing_Cleaning_Data_in_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a42c4d49",
   "metadata": {
    "papermill": {
     "duration": 0.015448,
     "end_time": "2024-01-21T04:51:30.946810",
     "exception": false,
     "start_time": "2024-01-21T04:51:30.931362",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Laptop Pricing Dataset\n",
    "This dataset is a collection of features related to various laptops, such as brand, processor type, RAM, storage capacity, and other specifications. The dataset also includes the corresponding prices of these laptops. This dataset can be used for regression analysis to predict the prices of laptops based on their features. \n",
    "\n",
    "### Project goal: cleaning the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b8c9ac",
   "metadata": {
    "papermill": {
     "duration": 0.016075,
     "end_time": "2024-01-21T04:51:30.979013",
     "exception": false,
     "start_time": "2024-01-21T04:51:30.962938",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1. Download libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d773a08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.013389Z",
     "iopub.status.busy": "2024-01-21T04:51:31.012999Z",
     "iopub.status.idle": "2024-01-21T04:51:31.460206Z",
     "shell.execute_reply": "2024-01-21T04:51:31.458935Z"
    },
    "papermill": {
     "duration": 0.466798,
     "end_time": "2024-01-21T04:51:31.463072",
     "exception": false,
     "start_time": "2024-01-21T04:51:30.996274",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836156d6",
   "metadata": {
    "papermill": {
     "duration": 0.015056,
     "end_time": "2024-01-21T04:51:31.493879",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.478823",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2. Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "17390f44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.528880Z",
     "iopub.status.busy": "2024-01-21T04:51:31.528312Z",
     "iopub.status.idle": "2024-01-21T04:51:31.534022Z",
     "shell.execute_reply": "2024-01-21T04:51:31.532770Z"
    },
    "papermill": {
     "duration": 0.026942,
     "end_time": "2024-01-21T04:51:31.536391",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.509449",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "url = \"/kaggle/input/laptop-pricing/laptop_pricing_dataset.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20611f5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.569496Z",
     "iopub.status.busy": "2024-01-21T04:51:31.569066Z",
     "iopub.status.idle": "2024-01-21T04:51:31.593890Z",
     "shell.execute_reply": "2024-01-21T04:51:31.592125Z"
    },
    "papermill": {
     "duration": 0.04394,
     "end_time": "2024-01-21T04:51:31.596493",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.552553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cb73d67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.628597Z",
     "iopub.status.busy": "2024-01-21T04:51:31.628216Z",
     "iopub.status.idle": "2024-01-21T04:51:31.660350Z",
     "shell.execute_reply": "2024-01-21T04:51:31.659422Z"
    },
    "papermill": {
     "duration": 0.050821,
     "end_time": "2024-01-21T04:51:31.662589",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.611768",
     "status": "completed"
    },
    "tags": []
   },
   "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>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",
       "      <td>35.560</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.60</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first five rows of the data:\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "700a24a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.695238Z",
     "iopub.status.busy": "2024-01-21T04:51:31.694856Z",
     "iopub.status.idle": "2024-01-21T04:51:31.709133Z",
     "shell.execute_reply": "2024-01-21T04:51:31.708175Z"
    },
    "papermill": {
     "duration": 0.03313,
     "end_time": "2024-01-21T04:51:31.711573",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.678443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Update the Screen_Size_cm column such that all values are rounded to nearest 2 decimal places:\n",
    "\n",
    "df[['Screen_Size_cm']] = np.round(df[['Screen_Size_cm']],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2cc6a677",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.745884Z",
     "iopub.status.busy": "2024-01-21T04:51:31.745466Z",
     "iopub.status.idle": "2024-01-21T04:51:31.764733Z",
     "shell.execute_reply": "2024-01-21T04:51:31.763525Z"
    },
    "papermill": {
     "duration": 0.03986,
     "end_time": "2024-01-21T04:51:31.767162",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.727302",
     "status": "completed"
    },
    "tags": []
   },
   "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>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",
       "      <td>35.56</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.60</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <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>39.62</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>39.62</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>2.20</td>\n",
       "      <td>946</td>\n",
       "    </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>33.78</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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    " \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4abb79b",
   "metadata": {
    "papermill": {
     "duration": 0.015863,
     "end_time": "2024-01-21T04:51:31.798825",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.782962",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. Identify and handle missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e19b95f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.833313Z",
     "iopub.status.busy": "2024-01-21T04:51:31.832923Z",
     "iopub.status.idle": "2024-01-21T04:51:31.838212Z",
     "shell.execute_reply": "2024-01-21T04:51:31.837331Z"
    },
    "papermill": {
     "duration": 0.026678,
     "end_time": "2024-01-21T04:51:31.842083",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.815405",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Evaluate for Missing Data:\n",
    "\n",
    "missing_data = df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf031420",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.943262Z",
     "iopub.status.busy": "2024-01-21T04:51:31.942329Z",
     "iopub.status.idle": "2024-01-21T04:51:31.954419Z",
     "shell.execute_reply": "2024-01-21T04:51:31.953153Z"
    },
    "papermill": {
     "duration": 0.032313,
     "end_time": "2024-01-21T04:51:31.957260",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.924947",
     "status": "completed"
    },
    "tags": []
   },
   "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"
     ]
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "print(missing_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b91145db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:31.991450Z",
     "iopub.status.busy": "2024-01-21T04:51:31.991007Z",
     "iopub.status.idle": "2024-01-21T04:51:32.013351Z",
     "shell.execute_reply": "2024-01-21T04:51:32.012173Z"
    },
    "papermill": {
     "duration": 0.042582,
     "end_time": "2024-01-21T04:51:32.015829",
     "exception": false,
     "start_time": "2024-01-21T04:51:31.973247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": [
    "# Count missing values in each column:\n",
    "\n",
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print (missing_data[column].value_counts())\n",
    "    print(\"\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c519d626",
   "metadata": {
    "papermill": {
     "duration": 0.016694,
     "end_time": "2024-01-21T04:51:32.048801",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.032107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Conclusion:\n",
    "Based on the summary above, each column has 238 rows of data and two of the columns containing missing data:\n",
    "\n",
    "\"Weight_kg\": 5 missing data\n",
    "\"Screen_Size_cm\": 4 missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d394bb2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.084739Z",
     "iopub.status.busy": "2024-01-21T04:51:32.084115Z",
     "iopub.status.idle": "2024-01-21T04:51:32.090591Z",
     "shell.execute_reply": "2024-01-21T04:51:32.089395Z"
    },
    "papermill": {
     "duration": 0.027121,
     "end_time": "2024-01-21T04:51:32.093171",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.066050",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of Weight_kg: 1.8622317596566522\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean value for the \"Weight_kg\" column:\n",
    "\n",
    "avg_weight= df['Weight_kg'].astype('float').mean(axis=0)\n",
    "print(\"Average of Weight_kg:\", avg_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67f69541",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.127928Z",
     "iopub.status.busy": "2024-01-21T04:51:32.127522Z",
     "iopub.status.idle": "2024-01-21T04:51:32.133492Z",
     "shell.execute_reply": "2024-01-21T04:51:32.132383Z"
    },
    "papermill": {
     "duration": 0.026235,
     "end_time": "2024-01-21T04:51:32.136218",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.109983",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace \"NaN\" with mean value in \"Weight_kg\" column:\n",
    "\n",
    "df['Weight_kg'].replace(np.nan, avg_weight, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "afafea5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.171101Z",
     "iopub.status.busy": "2024-01-21T04:51:32.170640Z",
     "iopub.status.idle": "2024-01-21T04:51:32.176417Z",
     "shell.execute_reply": "2024-01-21T04:51:32.175235Z"
    },
    "papermill": {
     "duration": 0.026918,
     "end_time": "2024-01-21T04:51:32.179459",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.152541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculate the most common value in the \"Screen_Size_cm\" column:\n",
    "\n",
    "common_screen_size = df['Screen_Size_cm'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f83caaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.215048Z",
     "iopub.status.busy": "2024-01-21T04:51:32.214585Z",
     "iopub.status.idle": "2024-01-21T04:51:32.220461Z",
     "shell.execute_reply": "2024-01-21T04:51:32.219180Z"
    },
    "papermill": {
     "duration": 0.02654,
     "end_time": "2024-01-21T04:51:32.222715",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.196175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace the missing 'Screen_Size_cm' values by the most frequent:\n",
    "\n",
    "df[\"Screen_Size_cm\"].replace(np.nan, common_screen_size, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f30cac1d",
   "metadata": {
    "papermill": {
     "duration": 0.016543,
     "end_time": "2024-01-21T04:51:32.255791",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.239248",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. Correct data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "364c95b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.290625Z",
     "iopub.status.busy": "2024-01-21T04:51:32.290210Z",
     "iopub.status.idle": "2024-01-21T04:51:32.298922Z",
     "shell.execute_reply": "2024-01-21T04:51:32.297638Z"
    },
    "papermill": {
     "duration": 0.029215,
     "end_time": "2024-01-21T04:51:32.301225",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.272010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          int64\n",
       "Manufacturer       object\n",
       "Category            int64\n",
       "Screen             object\n",
       "GPU                 int64\n",
       "OS                  int64\n",
       "CPU_core            int64\n",
       "Screen_Size_cm    float64\n",
       "CPU_frequency     float64\n",
       "RAM_GB              int64\n",
       "Storage_GB_SSD      int64\n",
       "Weight_kg         float64\n",
       "Price               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the data types for each column:\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f2c98db1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.337190Z",
     "iopub.status.busy": "2024-01-21T04:51:32.336754Z",
     "iopub.status.idle": "2024-01-21T04:51:32.345581Z",
     "shell.execute_reply": "2024-01-21T04:51:32.343862Z"
    },
    "papermill": {
     "duration": 0.02965,
     "end_time": "2024-01-21T04:51:32.348259",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.318609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert data types to proper format:\n",
    "\n",
    "df[['Screen_Size_cm', 'Weight_kg']] = df[['Screen_Size_cm', 'Weight_kg']].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d63529c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.386006Z",
     "iopub.status.busy": "2024-01-21T04:51:32.385559Z",
     "iopub.status.idle": "2024-01-21T04:51:32.393955Z",
     "shell.execute_reply": "2024-01-21T04:51:32.392887Z"
    },
    "papermill": {
     "duration": 0.030677,
     "end_time": "2024-01-21T04:51:32.396621",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.365944",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          int64\n",
       "Manufacturer       object\n",
       "Category            int64\n",
       "Screen             object\n",
       "GPU                 int64\n",
       "OS                  int64\n",
       "CPU_core            int64\n",
       "Screen_Size_cm    float64\n",
       "CPU_frequency     float64\n",
       "RAM_GB              int64\n",
       "Storage_GB_SSD      int64\n",
       "Weight_kg         float64\n",
       "Price               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3035b613",
   "metadata": {
    "papermill": {
     "duration": 0.015939,
     "end_time": "2024-01-21T04:51:32.429244",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.413305",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 5. Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b135adb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.465592Z",
     "iopub.status.busy": "2024-01-21T04:51:32.465169Z",
     "iopub.status.idle": "2024-01-21T04:51:32.471491Z",
     "shell.execute_reply": "2024-01-21T04:51:32.470097Z"
    },
    "papermill": {
     "duration": 0.02826,
     "end_time": "2024-01-21T04:51:32.474182",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.445922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Transform 'cm' to 'inch' in column \"Screen_Size_cm\" (1 inch = 2.54 cm):\n",
    "\n",
    "df[\"Screen_Size_cm\"] = df[\"Screen_Size_cm\"]/2.54"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "60339b33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.509949Z",
     "iopub.status.busy": "2024-01-21T04:51:32.509388Z",
     "iopub.status.idle": "2024-01-21T04:51:32.519202Z",
     "shell.execute_reply": "2024-01-21T04:51:32.517993Z"
    },
    "papermill": {
     "duration": 0.031012,
     "end_time": "2024-01-21T04:51:32.521793",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.490781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Rename the column:\n",
    "\n",
    "df.rename(columns={'Screen_Size_cm':'Screen_Size_inch'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9e78ff6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.558033Z",
     "iopub.status.busy": "2024-01-21T04:51:32.557617Z",
     "iopub.status.idle": "2024-01-21T04:51:32.563193Z",
     "shell.execute_reply": "2024-01-21T04:51:32.562337Z"
    },
    "papermill": {
     "duration": 0.026405,
     "end_time": "2024-01-21T04:51:32.565373",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.538968",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Transform 'kg' to 'pounds' in column \"Weight_kg\" (1 kg = 2.205 pounds):\n",
    "\n",
    "df[\"Weight_kg\"] = df[\"Weight_kg\"]*2.205"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2150f078",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.601130Z",
     "iopub.status.busy": "2024-01-21T04:51:32.600708Z",
     "iopub.status.idle": "2024-01-21T04:51:32.606359Z",
     "shell.execute_reply": "2024-01-21T04:51:32.605281Z"
    },
    "papermill": {
     "duration": 0.025908,
     "end_time": "2024-01-21T04:51:32.608698",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.582790",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Rename the column:\n",
    "\n",
    "df.rename(columns={'Weight_kg':'Weight_pounds'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "265c45aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.644508Z",
     "iopub.status.busy": "2024-01-21T04:51:32.644104Z",
     "iopub.status.idle": "2024-01-21T04:51:32.652156Z",
     "shell.execute_reply": "2024-01-21T04:51:32.650946Z"
    },
    "papermill": {
     "duration": 0.029235,
     "end_time": "2024-01-21T04:51:32.654566",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.625331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Update the \"Screen_Size_cm\" and \"Weight_pounds\" columns such that all values are rounded to nearest 2 decimal places \n",
    "df[['Screen_Size_inch', 'Weight_pounds']] = np.round(df[['Screen_Size_inch', 'Weight_pounds']], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6eea002f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.689918Z",
     "iopub.status.busy": "2024-01-21T04:51:32.689484Z",
     "iopub.status.idle": "2024-01-21T04:51:32.708895Z",
     "shell.execute_reply": "2024-01-21T04:51:32.707757Z"
    },
    "papermill": {
     "duration": 0.039704,
     "end_time": "2024-01-21T04:51:32.711384",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.671680",
     "status": "completed"
    },
    "tags": []
   },
   "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>Screen</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",
       "    </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",
       "      <td>14.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>3.53</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <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>15.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>4.85</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>15.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4.85</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>13.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>2.69</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>15.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4.21</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_inch  CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_pounds  \\\n",
       "0              14.0            1.6       8             256           3.53   \n",
       "1              15.6            2.0       4             256           4.85   \n",
       "2              15.6            2.7       8             256           4.85   \n",
       "3              13.3            1.6       8             128           2.69   \n",
       "4              15.6            1.8       8             256           4.21   \n",
       "\n",
       "   Price  \n",
       "0    978  \n",
       "1    634  \n",
       "2    946  \n",
       "3   1244  \n",
       "4    837  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the transformed data:\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d6d005",
   "metadata": {
    "papermill": {
     "duration": 0.017395,
     "end_time": "2024-01-21T04:51:32.746302",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.728907",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 6. Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0b3c22d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.782391Z",
     "iopub.status.busy": "2024-01-21T04:51:32.781969Z",
     "iopub.status.idle": "2024-01-21T04:51:32.787960Z",
     "shell.execute_reply": "2024-01-21T04:51:32.786921Z"
    },
    "papermill": {
     "duration": 0.02666,
     "end_time": "2024-01-21T04:51:32.790447",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.763787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Scale the column \"CPU_frequency\":\n",
    "\n",
    "df['CPU_frequency'] = df['CPU_frequency']/df['CPU_frequency'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5688cdc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.825802Z",
     "iopub.status.busy": "2024-01-21T04:51:32.825438Z",
     "iopub.status.idle": "2024-01-21T04:51:32.832401Z",
     "shell.execute_reply": "2024-01-21T04:51:32.831228Z"
    },
    "papermill": {
     "duration": 0.02786,
     "end_time": "2024-01-21T04:51:32.834972",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.807112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Update the CPU_frequency column such that all values are rounded to nearest 2 decimal places:\n",
    "\n",
    "df[['CPU_frequency']] = np.round(df[['CPU_frequency']], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f13bc0a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.871117Z",
     "iopub.status.busy": "2024-01-21T04:51:32.870708Z",
     "iopub.status.idle": "2024-01-21T04:51:32.881933Z",
     "shell.execute_reply": "2024-01-21T04:51:32.880836Z"
    },
    "papermill": {
     "duration": 0.032528,
     "end_time": "2024-01-21T04:51:32.884394",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.851866",
     "status": "completed"
    },
    "tags": []
   },
   "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>CPU_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CPU_frequency\n",
       "0           0.55\n",
       "1           0.69\n",
       "2           0.93\n",
       "3           0.55\n",
       "4           0.62"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the normalized columns:\n",
    "\n",
    "df[[\"CPU_frequency\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a6b31f",
   "metadata": {
    "papermill": {
     "duration": 0.016709,
     "end_time": "2024-01-21T04:51:32.918515",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.901806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 7. Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d42db31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:32.955209Z",
     "iopub.status.busy": "2024-01-21T04:51:32.954772Z",
     "iopub.status.idle": "2024-01-21T04:51:33.344274Z",
     "shell.execute_reply": "2024-01-21T04:51:33.343053Z"
    },
    "papermill": {
     "duration": 0.41142,
     "end_time": "2024-01-21T04:51:33.347205",
     "exception": false,
     "start_time": "2024-01-21T04:51:32.935785",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram of \"Price\" column to see what the distribution looks like:\n",
    "\n",
    "plt.pyplot.hist(df[\"Price\"])\n",
    "plt.pyplot.xlabel(\"Price\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "887970d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:33.386364Z",
     "iopub.status.busy": "2024-01-21T04:51:33.384301Z",
     "iopub.status.idle": "2024-01-21T04:51:33.390499Z",
     "shell.execute_reply": "2024-01-21T04:51:33.389679Z"
    },
    "papermill": {
     "duration": 0.028243,
     "end_time": "2024-01-21T04:51:33.393423",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.365180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Rearrange the values into three ‘bins':\n",
    "\n",
    "bins = np.linspace(min(df[\"Price\"]), max(df[\"Price\"]), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "707e377e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:33.433809Z",
     "iopub.status.busy": "2024-01-21T04:51:33.432735Z",
     "iopub.status.idle": "2024-01-21T04:51:33.440542Z",
     "shell.execute_reply": "2024-01-21T04:51:33.439297Z"
    },
    "papermill": {
     "duration": 0.031792,
     "end_time": "2024-01-21T04:51:33.443099",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.411307",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 527.        , 1621.33333333, 2715.66666667, 3810.        ])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the bins:\n",
    "\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0fa1e87e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:33.481221Z",
     "iopub.status.busy": "2024-01-21T04:51:33.480774Z",
     "iopub.status.idle": "2024-01-21T04:51:33.485671Z",
     "shell.execute_reply": "2024-01-21T04:51:33.484598Z"
    },
    "papermill": {
     "duration": 0.026565,
     "end_time": "2024-01-21T04:51:33.488089",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.461524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set group names:\n",
    "\n",
    "group_names = ['Low', 'Medium', 'High']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4582452e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:33.524708Z",
     "iopub.status.busy": "2024-01-21T04:51:33.524309Z",
     "iopub.status.idle": "2024-01-21T04:51:33.532186Z",
     "shell.execute_reply": "2024-01-21T04:51:33.531151Z"
    },
    "papermill": {
     "duration": 0.028879,
     "end_time": "2024-01-21T04:51:33.534416",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.505537",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Determine what each value of df['Price'] belongs to:\n",
    "\n",
    "df['Price-binned'] = pd.cut(df['Price'], bins, labels=group_names, include_lowest=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b188d38b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:33.573224Z",
     "iopub.status.busy": "2024-01-21T04:51:33.572491Z",
     "iopub.status.idle": "2024-01-21T04:51:33.586794Z",
     "shell.execute_reply": "2024-01-21T04:51:33.585580Z"
    },
    "papermill": {
     "duration": 0.036436,
     "end_time": "2024-01-21T04:51:33.589495",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.553059",
     "status": "completed"
    },
    "tags": []
   },
   "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>Price</th>\n",
       "      <th>Price-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>978</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>634</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>946</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1244</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>837</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price Price-binned\n",
       "0    978          Low\n",
       "1    634          Low\n",
       "2    946          Low\n",
       "3   1244          Low\n",
       "4    837          Low"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "df[['Price','Price-binned']].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2289edab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:33.629211Z",
     "iopub.status.busy": "2024-01-21T04:51:33.628023Z",
     "iopub.status.idle": "2024-01-21T04:51:33.638820Z",
     "shell.execute_reply": "2024-01-21T04:51:33.637264Z"
    },
    "papermill": {
     "duration": 0.033209,
     "end_time": "2024-01-21T04:51:33.641611",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.608402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price-binned\n",
       "Low       160\n",
       "Medium     72\n",
       "High        6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of values in each bin:\n",
    "\n",
    "df[\"Price-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7535b2cf",
   "metadata": {
    "papermill": {
     "duration": 0.017704,
     "end_time": "2024-01-21T04:51:33.677525",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.659821",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Plot the bar graph of these bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "be9a054b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:33.715354Z",
     "iopub.status.busy": "2024-01-21T04:51:33.714965Z",
     "iopub.status.idle": "2024-01-21T04:51:34.025228Z",
     "shell.execute_reply": "2024-01-21T04:51:34.023925Z"
    },
    "papermill": {
     "duration": 0.331921,
     "end_time": "2024-01-21T04:51:34.027755",
     "exception": false,
     "start_time": "2024-01-21T04:51:33.695834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price bins')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of each bin:\n",
    "\n",
    "pyplot.bar(group_names, df[\"Price-binned\"].value_counts())\n",
    "plt.pyplot.xlabel(\"Price\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"Price bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090a04d1",
   "metadata": {
    "papermill": {
     "duration": 0.020167,
     "end_time": "2024-01-21T04:51:34.066779",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.046612",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 8. Indicator variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "885735e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:34.106488Z",
     "iopub.status.busy": "2024-01-21T04:51:34.105754Z",
     "iopub.status.idle": "2024-01-21T04:51:34.114071Z",
     "shell.execute_reply": "2024-01-21T04:51:34.112687Z"
    },
    "papermill": {
     "duration": 0.031398,
     "end_time": "2024-01-21T04:51:34.117010",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.085612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert \"Screen\" to indicator variables and assign it to data \"dummy_variable_1\":\n",
    "\n",
    "dummy_variable_1 = pd.get_dummies(df[\"Screen\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "00f98ca6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:34.158735Z",
     "iopub.status.busy": "2024-01-21T04:51:34.158318Z",
     "iopub.status.idle": "2024-01-21T04:51:34.164057Z",
     "shell.execute_reply": "2024-01-21T04:51:34.162904Z"
    },
    "papermill": {
     "duration": 0.029482,
     "end_time": "2024-01-21T04:51:34.166404",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.136922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Change the column names:\n",
    "\n",
    "dummy_variable_1.rename(columns={'IPS Panel':'Screen-IPS_panel', 'Full HD':'Screen-Full_HD'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0cde2ca4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:34.206386Z",
     "iopub.status.busy": "2024-01-21T04:51:34.205990Z",
     "iopub.status.idle": "2024-01-21T04:51:34.217044Z",
     "shell.execute_reply": "2024-01-21T04:51:34.215927Z"
    },
    "papermill": {
     "duration": 0.033955,
     "end_time": "2024-01-21T04:51:34.219568",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.185613",
     "status": "completed"
    },
    "tags": []
   },
   "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>Screen-Full_HD</th>\n",
       "      <th>Screen-IPS_panel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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": [
    "# Check the result:\n",
    "\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1187d540",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:34.261388Z",
     "iopub.status.busy": "2024-01-21T04:51:34.261003Z",
     "iopub.status.idle": "2024-01-21T04:51:34.267307Z",
     "shell.execute_reply": "2024-01-21T04:51:34.266052Z"
    },
    "papermill": {
     "duration": 0.029402,
     "end_time": "2024-01-21T04:51:34.269485",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.240083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merge data \"df\" and \"dummy_variable_1\":\n",
    "\n",
    "df = pd.concat([df, dummy_variable_1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "685f33f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:34.311136Z",
     "iopub.status.busy": "2024-01-21T04:51:34.310750Z",
     "iopub.status.idle": "2024-01-21T04:51:34.316306Z",
     "shell.execute_reply": "2024-01-21T04:51:34.315431Z"
    },
    "papermill": {
     "duration": 0.029142,
     "end_time": "2024-01-21T04:51:34.318707",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.289565",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Drop original column \"Screen\" from \"df\"\n",
    "\n",
    "df.drop(\"Screen\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "35035111",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:34.359772Z",
     "iopub.status.busy": "2024-01-21T04:51:34.359135Z",
     "iopub.status.idle": "2024-01-21T04:51:34.371935Z",
     "shell.execute_reply": "2024-01-21T04:51:34.370544Z"
    },
    "papermill": {
     "duration": 0.036258,
     "end_time": "2024-01-21T04:51:34.374656",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.338398",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0 Manufacturer  Category  GPU  OS  CPU_core  Screen_Size_inch  \\\n",
      "0           0         Acer         4    2   1         5              14.0   \n",
      "1           1         Dell         3    1   1         3              15.6   \n",
      "2           2         Dell         3    1   1         7              15.6   \n",
      "3           3         Dell         4    2   1         5              13.3   \n",
      "4           4           HP         4    2   1         7              15.6   \n",
      "\n",
      "   CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_pounds  Price Price-binned  \\\n",
      "0           0.55       8             256           3.53    978          Low   \n",
      "1           0.69       4             256           4.85    634          Low   \n",
      "2           0.93       8             256           4.85    946          Low   \n",
      "3           0.55       8             128           2.69   1244          Low   \n",
      "4           0.62       8             256           4.21    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  \n"
     ]
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "407c8d7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-21T04:51:34.415637Z",
     "iopub.status.busy": "2024-01-21T04:51:34.415209Z",
     "iopub.status.idle": "2024-01-21T04:51:34.426924Z",
     "shell.execute_reply": "2024-01-21T04:51:34.425673Z"
    },
    "papermill": {
     "duration": 0.035408,
     "end_time": "2024-01-21T04:51:34.429565",
     "exception": false,
     "start_time": "2024-01-21T04:51:34.394157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Save the file:\n",
    "\n",
    "df.to_csv('laptop_pricing_cleaned.csv')"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 4313257,
     "sourceId": 7414679,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30635,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 7.53117,
   "end_time": "2024-01-21T04:51:34.971826",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-01-21T04:51:27.440656",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}