PORTFOLIO PROJECT 2. PIZZA PREDICTION PREDICTION.ipynb

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  {
   "cell_type": "markdown",
   "id": "96b7202c-850a-4b78-952a-18e2f698d42d",
   "metadata": {},
   "source": [
    "# Pizza Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5c5f12-ada3-41fc-bf22-5e16edc7e173",
   "metadata": {},
   "source": [
    "### Data Source: Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d6067a-3613-47a2-aa3e-908a6a79add2",
   "metadata": {},
   "source": [
    "Introduction\n",
    "\n",
    "A new pizza company wants to startup in the area and wants to understand the pices of its competitors. This analysis will help the company determine the price of their new pizza menu that is launching without affecting sales or profits.\n",
    "\n",
    "Objective\n",
    "\n",
    "Determine which features affects the price of the pizza\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62343bf7-8815-49fb-9ad0-46acf72d1a25",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc747cba-7747-4fb9-b826-4ae3bde2aa58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Structure\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Others\n",
    "sns.set(style='darkgrid')\n",
    "sns.set_theme()\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4290988-1bd4-4c03-8059-0f4432d788be",
   "metadata": {},
   "source": [
    "## Loading The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "790b777e-c813-4312-be55-cbda9fb1e956",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price_rupiah</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp235,000</td>\n",
       "      <td>22 inch</td>\n",
       "      <td>chicken</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp198,000</td>\n",
       "      <td>20 inch</td>\n",
       "      <td>papperoni</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp120,000</td>\n",
       "      <td>16 inch</td>\n",
       "      <td>mushrooms</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp155,000</td>\n",
       "      <td>14 inch</td>\n",
       "      <td>smoked_beef</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp248,000</td>\n",
       "      <td>18 inch</td>\n",
       "      <td>mozzarella</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company price_rupiah diameter      topping           variant     size  \\\n",
       "0       A    Rp235,000  22 inch      chicken  double_signature    jumbo   \n",
       "1       A    Rp198,000  20 inch    papperoni  double_signature    jumbo   \n",
       "2       A    Rp120,000  16 inch    mushrooms  double_signature  reguler   \n",
       "3       A    Rp155,000  14 inch  smoked_beef  double_signature  reguler   \n",
       "4       A    Rp248,000  18 inch   mozzarella  double_signature    jumbo   \n",
       "\n",
       "  extra_sauce extra_cheese extra_mushrooms  \n",
       "0         yes          yes              no  \n",
       "1         yes          yes              no  \n",
       "2         yes          yes             yes  \n",
       "3         yes           no             yes  \n",
       "4         yes           no             yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Henry Oguns\\Desktop\\Data Science Portfolio\\datasets\\pizza_v2.csv\")\n",
    "pd.set_option(\"display.max_columns\",None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40206071-cd40-4c24-bf6e-d97372105c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39dfe23-2d81-49e1-8907-5e1854204a02",
   "metadata": {},
   "source": [
    "Dataset has 129 observations and 9 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f51573a-48de-4d2a-ba4d-24c67d130a47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 129 entries, 0 to 128\n",
      "Data columns (total 9 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   company          129 non-null    object\n",
      " 1   price_rupiah     129 non-null    object\n",
      " 2   diameter         129 non-null    object\n",
      " 3   topping          129 non-null    object\n",
      " 4   variant          129 non-null    object\n",
      " 5   size             129 non-null    object\n",
      " 6   extra_sauce      129 non-null    object\n",
      " 7   extra_cheese     129 non-null    object\n",
      " 8   extra_mushrooms  129 non-null    object\n",
      "dtypes: object(9)\n",
      "memory usage: 9.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking the data types in the dataset\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25d2883c-34b9-46ef-b549-f148b412868a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company            0\n",
       "price_rupiah       0\n",
       "diameter           0\n",
       "topping            0\n",
       "variant            0\n",
       "size               0\n",
       "extra_sauce        0\n",
       "extra_cheese       0\n",
       "extra_mushrooms    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3fc05840-28da-48c0-b19e-82f9c8c29040",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6682285-2cc4-4977-a45a-306083844f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93df29e3-3fc0-41cb-974a-d63b53a95437",
   "metadata": {},
   "source": [
    "The dataset contanins 0 Null and 2 Duplicates Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "990e84dc-ceea-4dbe-8a8d-b0d7c6c3b451",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price_rupiah</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
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       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
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       "      <td>12</td>\n",
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       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>C</td>\n",
       "      <td>Rp72,000</td>\n",
       "      <td>12 inch</td>\n",
       "      <td>chicken</td>\n",
       "      <td>classic</td>\n",
       "      <td>medium</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>49</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>76</td>\n",
       "      <td>84</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company price_rupiah diameter  topping  variant    size extra_sauce  \\\n",
       "count      127          127      127      127      127     127         127   \n",
       "unique       5           43       12       12       20       6           2   \n",
       "top          C     Rp72,000  12 inch  chicken  classic  medium         yes   \n",
       "freq        30           11       49       28       30      41          76   \n",
       "\n",
       "       extra_cheese extra_mushrooms  \n",
       "count           127             127  \n",
       "unique            2               2  \n",
       "top             yes             yes  \n",
       "freq             84              71  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understandin the overall statistic of the dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef4f1ee-8003-4987-98d0-423e97e59b91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4a668726-4024-41f4-9252-e39c3bc1ac31",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d6cd7ef-6b52-4edb-a70e-138dd862eca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price_rupiah</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp235,000</td>\n",
       "      <td>22 inch</td>\n",
       "      <td>chicken</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp198,000</td>\n",
       "      <td>20 inch</td>\n",
       "      <td>papperoni</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp120,000</td>\n",
       "      <td>16 inch</td>\n",
       "      <td>mushrooms</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp155,000</td>\n",
       "      <td>14 inch</td>\n",
       "      <td>smoked_beef</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp248,000</td>\n",
       "      <td>18 inch</td>\n",
       "      <td>mozzarella</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  company price_rupiah diameter      topping           variant     size  \\\n",
       "0       A    Rp235,000  22 inch      chicken  double_signature    jumbo   \n",
       "1       A    Rp198,000  20 inch    papperoni  double_signature    jumbo   \n",
       "2       A    Rp120,000  16 inch    mushrooms  double_signature  reguler   \n",
       "3       A    Rp155,000  14 inch  smoked_beef  double_signature  reguler   \n",
       "4       A    Rp248,000  18 inch   mozzarella  double_signature    jumbo   \n",
       "\n",
       "  extra_sauce extra_cheese extra_mushrooms  \n",
       "0         yes          yes              no  \n",
       "1         yes          yes              no  \n",
       "2         yes          yes             yes  \n",
       "3         yes           no             yes  \n",
       "4         yes           no             yes  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04820f7b-a730-471c-8bad-b7f45be37487",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the price_rupiah column to price\n",
    "\n",
    "df = df.rename({'price_rupiah' : 'price'}, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89ead9da-3152-440e-905a-be442468cee9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>235000</td>\n",
       "      <td>22 inch</td>\n",
       "      <td>chicken</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>198000</td>\n",
       "      <td>20 inch</td>\n",
       "      <td>papperoni</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>120000</td>\n",
       "      <td>16 inch</td>\n",
       "      <td>mushrooms</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>155000</td>\n",
       "      <td>14 inch</td>\n",
       "      <td>smoked_beef</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>248000</td>\n",
       "      <td>18 inch</td>\n",
       "      <td>mozzarella</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company   price diameter      topping           variant     size  \\\n",
       "0       A  235000  22 inch      chicken  double_signature    jumbo   \n",
       "1       A  198000  20 inch    papperoni  double_signature    jumbo   \n",
       "2       A  120000  16 inch    mushrooms  double_signature  reguler   \n",
       "3       A  155000  14 inch  smoked_beef  double_signature  reguler   \n",
       "4       A  248000  18 inch   mozzarella  double_signature    jumbo   \n",
       "\n",
       "  extra_sauce extra_cheese extra_mushrooms  \n",
       "0         yes          yes              no  \n",
       "1         yes          yes              no  \n",
       "2         yes          yes             yes  \n",
       "3         yes           no             yes  \n",
       "4         yes           no             yes  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'] = df['price'].str.replace('Rp', '')\n",
    "df['price'] = df['price'].str.replace(',', '').astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4012acea-feb1-41e2-893b-1aa2371dcf45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Currency conversion\n",
    "\n",
    "def convert (value):\n",
    "    return value*0.0054"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3eeedcff-16d7-416a-8cd3-4b95d39def39",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['price'] = df['price'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c26c94a0-0661-41b9-a3c9-e285d659ba60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>22 inch</td>\n",
       "      <td>chicken</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>1069.2</td>\n",
       "      <td>20 inch</td>\n",
       "      <td>papperoni</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company   price diameter    topping           variant   size extra_sauce  \\\n",
       "0       A  1269.0  22 inch    chicken  double_signature  jumbo         yes   \n",
       "1       A  1069.2  20 inch  papperoni  double_signature  jumbo         yes   \n",
       "\n",
       "  extra_cheese extra_mushrooms  \n",
       "0          yes              no  \n",
       "1          yes              no  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd980f52-eb14-4ac6-af73-a1b0b2128701",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing 'inch' and  ',' from the diameter columns\n",
    "\n",
    "df['diameter'] = df['diameter'].str.replace('inch', '')\n",
    "df['diameter'] = df['diameter'].str.replace('', '').astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f82718b4-39be-466a-bb94-dae1ed319455",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f1b84f26-d825-491e-9e0d-825942e303db",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abc402f1-345f-4375-ab26-cbd503318db9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['company', 'price', 'diameter', 'topping', 'variant', 'size',\n",
       "       'extra_sauce', 'extra_cheese', 'extra_mushrooms'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddfb6616-d47b-416b-8bfd-347b95f1967b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company\n",
       "C    30\n",
       "E    28\n",
       "A    25\n",
       "B    24\n",
       "D    20\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of data from each of the companies\n",
    "\n",
    "df.company.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ca10abe-ce0e-4998-8083-58f450f730df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Price distribution across the dataset\n",
    "\n",
    "plt.hist(x = df.price)\n",
    "plt.title('Price Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "64e693fd-244c-400c-9f0c-51b174e04bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Topping distribution across the dataset\n",
    "\n",
    "sns.countplot(y = df.topping)\n",
    "plt.title('Toppings Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff94ec44-3a37-4298-8619-f90d6fe030bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extra Sauce distribution across the dataset\n",
    "\n",
    "\n",
    "sns.countplot(x = df['extra_sauce'])\n",
    "plt.title('Extra Sauce Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "53703da5-0ad0-403d-8b49-b6c0a55b9254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hjh1TaGio3n77bduSnbu7u2bMmKFx48ZpwIABKlOmjJ5//nkdPXpUqamptn107NhR+/fv14svvqjx48ff9KnJDRs21OLFizVjxgwNGDBAXl5eqlu3rt56661bXm8FuDI3gyu/AACASXCNDQAAMA2CDQAAMA2CDQAAMA2CDQAAMA2CDQAAMA2CDQAAMI3/yc+xMQxDVivvcgcAoKiwWNxu+YW0v/U/GWysVkOZmffm23EBAEDhK126hNzdbx9sWIoCAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACm4eHsAszKYnGTxeLm7DIAl2K1GrJaDWeXAcDECDaFwGJxU6lSPnJ3Z0IM+K28PKvOn79MuAFQaAg2hcBicZO7u0Wz39uqExkXnF0O4BLKB/kr7rlGsljcCDYACg3BphCdyLign0+cc3YZAAD8z2CtBAAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmIbTg01ubq6mT5+u5s2bq3bt2urYsaP27dtn6z9w4IA6deqkWrVqKTo6WkuXLnVesQAAwKU5PdjMnTtX77//vsaMGaN169apUqVK6tGjhzIyMnTu3Dl17dpVFSpU0Jo1axQXF6eEhAStWbPG2WUDAAAX5PTvitq4caOeeOIJNW7cWJI0dOhQvf/++9q3b59SU1Pl6emp0aNHy8PDQ5UrV9axY8e0YMECxcbGOrlyAADgapw+YxMYGKhNmzbp+PHjysvL06pVq+Tl5aWqVasqOTlZUVFR8vD4b/5q0KCBfv75Z505c8aJVQMAAFfk9BmbYcOG6ZVXXtFf/vIXubu7y2KxaObMmapQoYLS09MVGhpqt31QUJAkKS0tTWXKlHF4XA+Pwst07u5Oz4uAy+L8AFCYnB5sDh8+rJIlS2r27NkKDg7W+++/r0GDBmn58uXKzs6Wl5eX3fbFihWTJF29etXhMS0WNwUElLirugE4xs+vuLNLAGBiTg02aWlpGjhwoJYsWaK6detKkiIjI3X48GHNnDlT3t7eysnJsbvPjUDj4+Pj8LhWq6GsrMuOF34b7u4WXryBW8jKuqK8PKuzywBQxPj5FS/QjK9Tg83333+va9euKTIy0q69Zs2a+uabb3TfffcpIyPDru/G7eDg4LsaOzeXF1bAGfLyrJx/AAqNUxe7Q0JCJEn//ve/7doPHTqkihUrql69etq9e7fy8vJsfUlJSapUqZICAwP/1FoBAIDrc2qwqVGjhh555BENGTJESUlJ+vnnnzVt2jRt375dPXv2VGxsrC5duqRhw4bp8OHDWrt2rZYsWaJevXo5s2wAAOCinLoUZbFYNHfuXE2bNk2vv/66Lly4oNDQUC1ZskQ1a9aUJCUmJmrs2LGKiYlR2bJlNXjwYMXExDizbAAA4KLcDMMwnF3Eny0vz6rMzF8Lbf8eHhYFBJTQP6Z/qp9PnCu0cYCipGL5AI17pbXOnfuVa2wA3LHSpUsU6OJhPlACAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYBsEGAACYhoczB9+xY4e6dOmSb9/999+vr7/+WsePH9eYMWO0a9cu+fj4qH379urXr5/c3d3/5GoBAICrc2qwqV27tr777ju7tn379qlfv37q27evrl27pu7du6tixYpauXKl/vOf/2jYsGGyWCx6+eWXnVQ1AABwVU4NNl5eXipbtqzt9uXLlzV+/HjFxMQoNjZWn3zyiU6ePKnVq1fL399foaGhOnv2rCZOnKjevXvLy8vLidUDAABX41LX2MybN09XrlzRkCFDJEnJyckKDw+Xv7+/bZsGDRro0qVLOnDggLPKBAAALsqpMza/lZmZqSVLlmjgwIEqVaqUJCk9PV0hISF22wUFBUmS0tLSVLNmTYfH8/AovEzn7u5SeRFwKZwfAAqTywSbFStWqGTJknr22WdtbdnZ2fLz87PbrlixYpKkq1evOjyWxeKmgIASDt8fgOP8/Io7uwQAJuYywWbdunV66qmn5O3tbWvz9vZWTk6O3XY3Ao2Pj4/DY1mthrKyLjt8/9txd7fw4g3cQlbWFeXlWZ1dBoAixs+veIFmfF0i2Bw8eFC//PKLnnzySbv2kJAQHTp0yK4tIyNDkhQcHHxXY+bm8sIKOENenpXzD0ChcYnF7uTkZAUGBqpq1ap27fXq1VNKSoouXbpka0tKSlKJEiVu2hYAAMAlgk1KSorCwsJuam/RooXKli2r/v376+DBg9q4caOmTJmibt268VZvAABwE5cINqdPn7a9E+q3ihUrpsTERFmtVj3zzDOKj49Xhw4d1Ldv3z+/SAAA4PJc4hqbt99++5Z9Dz74oBYtWvQnVgMAAIoql5ixAQAAuBcINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDQINgAAwDRcItisW7dOrVu3VmRkpNq0aaPPPvvM1nf8+HH16tVLderUUePGjTVt2jTl5eU5sVoAAOCqnB5sPvroIw0bNkwdO3bUhg0b9MQTT2jAgAHau3evrl27pu7du0uSVq5cqVGjRum9997T7NmznVw1AABwRR7OHNwwDE2fPl1dunRRx44dJUl9+vRRcnKydu7cqRMnTujkyZNavXq1/P39FRoaqrNnz2rixInq3bu3vLy8nFk+AABwMU6dsUlNTdWJEyf05JNP2rUvXLhQvXr1UnJyssLDw+Xv72/ra9CggS5duqQDBw782eUCAAAX59QZm9TUVEnS5cuX1b17d6WkpOj+++9Xnz59FB0drfT0dIWEhNjdJygoSJKUlpammjVrOjy2h0fhZTp3d6ev8AEui/MDQGFyarC5dOmSJGnIkCF66aWXNGjQIH3xxRfq27evFi9erOzsbPn5+dndp1ixYpKkq1evOjyuxeKmgIASjhcOwGF+fsWdXQIAE3NqsPH09JQkde/eXTExMZKkatWqKSUlRYsXL5a3t7dycnLs7nMj0Pj4+Dg8rtVqKCvrssP3vx13dwsv3sAtZGVdUV6e1dllAChi/PyKF2jG16nBJjg4WJIUGhpq116lShVt3rxZUVFROnTokF1fRkaG3X0dlZvLCyvgDHl5Vs4/AIXGqYvd4eHhKlGihL7//nu79kOHDqlChQqqV6+eUlJSbEtWkpSUlKQSJUqoatWqf3a5AADAxTk12Hh7e6tHjx6aPXu2PvnkE/3nP//R3LlztXXrVnXt2lUtWrRQ2bJl1b9/fx08eFAbN27UlClT1K1bN97qDQAAbuLUpShJ6tu3r4oXL66pU6fq1KlTqly5smbOnKn69etLkhITExUfH69nnnlG/v7+6tChg/r27evkqgEAgCtyerCRpK5du6pr16759j344INatGjRn1wRAAAoivhACQAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBoEGwAAYBpODzanTp1SWFjYTT9r166VJB04cECdOnVSrVq1FB0draVLlzq5YgAA4Ko8nF3AwYMHVaxYMW3cuFFubm629pIlS+rcuXPq2rWroqOjFR8fr3379ik+Pl4lSpRQbGysE6sGAACuyOnB5tChQ6pYsaKCgoJu6nvnnXfk6emp0aNHy8PDQ5UrV9axY8e0YMECgg0AALiJ04PNv//9b1WuXDnfvuTkZEVFRcnD479lNmjQQPPnz9eZM2dUpkwZh8f18Ci8VTh3d6ev8AEui/MDQGFyerA5dOiQAgIC1LFjR6WmpurBBx9Unz591KRJE6Wnpys0NNRu+xszO2lpaQ4HG4vFTQEBJe66dgB3zs+vuLNLAGBiTg02ubm5Onr0qKpUqaKhQ4fK19dXGzZsUM+ePbV48WJlZ2fLy8vL7j7FihWTJF29etXhca1WQ1lZl++q9j/i7m7hxRu4haysK8rLszq7DABFjJ9f8QLN+Do12Hh4eGjHjh1yd3eXt7e3JCkiIkI//fSTFi5cKG9vb+Xk5Njd50ag8fHxuauxc3N5YQWcIS/PyvkHoNA4fbG7RIkStlBzw8MPP6xTp04pJCREGRkZdn03bgcHB/9pNQIAgKLBqcHmp59+Up06dbRjxw679v3796tKlSqqV6+edu/erby8PFtfUlKSKlWqpMDAwD+7XAAA4OKcGmwqV66shx56SKNHj1ZycrKOHDmi8ePHa9++ferTp49iY2N16dIlDRs2TIcPH9batWu1ZMkS9erVy5llAwAAF+XUa2wsFovmzZunyZMnq3///srKylL16tW1ePFi27uhEhMTNXbsWMXExKhs2bIaPHiwYmJinFk2AABwUQ4Fm127dql69eoqUeLmt0xnZWXp22+/VZs2bQq0rzJlymj8+PG37K9Ro4ZWrVrlSJkAAOB/jENLUV26dNGRI0fy7UtJSdHrr79+V0UBAAA4osAzNkOGDFFaWpokyTAMjRo1Sr6+vjdt9/PPP9/VJwIDAAA4qsAzNq1atZJhGDIMw9Z24/aNH4vFolq1av3h0hIAAEBhKfCMTXR0tKKjoyVJnTt31qhRo275HU8AAADO4NDFw8uWLbvXdQAAANw1h4JNdna25s6dq02bNunKlSuyWu0/Ht3NzU0bN268JwUCAAAUlEPBZuzYsfrggw8UFRWlatWqyWJx+jczAAAAOBZsvvzyS7366qvq2bPnva4HAADAYQ5NtVy7dk01atS417UAAADcFYeCTePGjfXNN9/c61oAAADuikNLUa1bt9bIkSOVmZmpmjVrqnjx4jdt89RTT91tbQAAAHfEoWDTv39/SdK6deu0bt26m/rd3NwINgAA4E/nULD5+uuv73UdAAAAd82hYFO+fPl7XQcAAMBdcyjYzJo167bbvPTSS47sGgAAwGH3PNj4+voqKCiIYAMAAP50DgWbgwcP3tR2+fJlJScna9SoURo+fPhdFwYAAHCn7tl3Ifj4+KhJkyaKi4vTxIkT79VuAQAACuyef8nTfffdpyNHjtzr3QIAANyWQ0tR+TEMQ+np6UpMTORdUwAAwCkcCjZVq1aVm5tbvn2GYbAUBQAAnMKhYBMXF5dvsPH19VWzZs1UsWLFu60LAFyWxeImiyX//7kD/ldZrYasVsPZZTgWbPr163ev6wCAIsFicVOpUj5yd7/nlygCRVpenlXnz192erhx+BqbzMxMLVq0SDt37lRWVpYCAgJUt25dvfDCCwoMDLyXNQKAy7BY3OTubtHs97bqRMYFZ5cDuITyQf6Ke66RLBa3ohls0tPT9eyzzyozM1O1atVS9erVdfr0aS1evFjr1q3TBx98oODg4HtdKwC4jBMZF/TziXPOLgPA7zgUbCZNmiQPDw99+umneuCBB2ztv/zyi7p166apU6dqwoQJ96xIAACAgnBokfi7777Tyy+/bBdqJOmBBx5QXFycvvnmm3tSHAAAwJ1wKNjk5eUpICAg377SpUvr0qVLd1UUAACAIxwKNmFhYfr444/z7fvoo48UGhp6V0UBAAA4wqFrbPr27avu3bvrwoULat26tcqWLavTp09rw4YN+u677zRjxox7XScAAMBtOTRj06hRI02YMEEpKSkaOnSounfvrqFDh+rgwYMaP368WrZs6VAxqampql27ttauXWtrO3DggDp16qRatWopOjpaS5cudWjfAADA/Bz+hKmMjAxVr15dGzZs0IoVKzRixAjl5uY6fH3NtWvXNGjQIF2+fNnWdu7cOXXt2lUVKlTQmjVrFBcXp4SEBK1Zs8bRsgEAgIk5tBS1aNEiTZs2TZ06dVLlypUlSeXKldPRo0c1YcIEFStWTE8//fQd7XPmzJny9fW1a1u9erU8PT01evRoeXh4qHLlyjp27JgWLFig2NhYR0oHAAAm5lCwWblypfr376+ePXva2sqVK6c33nhDZcqU0ZIlS+4o2OzatUurVq3SunXr1KxZM1t7cnKyoqKi5OHx3zIbNGig+fPn68yZMypTpowj5UuSPDwK7+PQ+ah14NaK+vlR1OsHCpMrnB8OBZtTp04pMjIy376aNWtq7ty5Bd5XVlaWBg8erDfeeEPlypWz60tPT7/pHVZBQUGSpLS0NIeDjcXipoCAEg7dF8Dd8fMr7uwSABQSVzi/HQo25cuX1/bt29WwYcOb+nbt2qWQkJAC72vUqFGqXbu2nnzyyZv6srOz5eXlZddWrFgxSdLVq1fvsOr/sloNZWVdvv2GDnJ3t7jEkwu4oqysK8rLszq7DIdxfgO3Vpjnt59f8QLNCDkUbJ555hlNmjRJ165dU4sWLRQYGKjMzExt2rRJixcv1sCBAwu0n3Xr1ik5OfmWn4nj7e2tnJwcu7YbgcbHx8eR0m1yc4vuCytQlOXlWTn/AJNyhfPboWDzwgsv6NSpU1q2bJmWLFlia3d3d9fzzz+vrl27Fmg/a9as0dmzZ+2uq5GkkSNH6tNPP1VISIgyMjLs+m7c5ks2AQDA7zkUbCRpyJAh6tu3r/bt26fz58/Lz89PNWrUuOVXLeQnISFB2dnZdm1//etf9fLLL6tt27b66KOPtHLlSuXl5cnd3V2SlJSUpEqVKikwMNDR0gEAgEk5HGwkqWTJknrsscccvv+tZl0CAwMVHBys2NhYJSYmatiwYerRo4f+9a9/acmSJYqPj3d4TAAAYF7Of1/WHwgMDFRiYqJSU1MVExOjWbNmafDgwYqJiXF2aQAAwAXd1YxNYfj3v/9td7tGjRpatWqVk6oBAABFiUvP2AAAANwJgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANgg0AADANpwebs2fP6rXXXlODBg1Uu3Zt9ezZU0eOHLH1HzhwQJ06dVKtWrUUHR2tpUuXOrFaAADgypwebOLi4nTs2DEtWLBAH3zwgby9vfXCCy/oypUrOnfunLp27aoKFSpozZo1iouLU0JCgtasWePssgEAgAvycObgFy5cUPny5dWrVy+FhoZKkvr27au///3v+umnn7R9+3Z5enpq9OjR8vDwUOXKlW0hKDY21pmlAwAAF+TUYOPv76/JkyfbbmdmZmrJkiUKCQlRlSpVNHPmTEVFRcnD479lNmjQQPPnz9eZM2dUpkwZh8f28Ci8ySp3d6dPhAEuq6ifH0W9fqAwucL54dRg81vDhw/X6tWr5eXlpblz58rHx0fp6em2mZwbgoKCJElpaWkOBxuLxU0BASXuumYAd87Pr7izSwBQSFzh/HaZYPP888/r2Wef1bvvvqu4uDitWLFC2dnZ8vLystuuWLFikqSrV686PJbVaigr6/Jd1ftH3N0tLvHkAq4oK+uK8vKszi7DYZzfwK0V5vnt51e8QDNCLhNsqlSpIkkaO3asvv/+ey1fvlze3t7Kycmx2+5GoPHx8bmr8XJzi+4LK1CU5eVZOf8Ak3KF89upi2GZmZnasGGDcnNzbW0Wi0VVqlRRRkaGQkJClJGRYXefG7eDg4P/1FoBAIDrc2qwOXPmjAYMGKDt27fb2q5du6aUlBRVrlxZ9erV0+7du5WXl2frT0pKUqVKlRQYGOiMkgEAgAtzarAJDQ1VkyZN9Oabb2rXrl06dOiQhg4dqqysLL3wwguKjY3VpUuXNGzYMB0+fFhr167VkiVL1KtXL2eWDQAAXJTT35c1ZcoUNWzYUK+++qqefvppnT9/Xu+++67uu+8+BQYGKjExUampqYqJidGsWbM0ePBgxcTEOLtsAADggpx+8XDJkiU1atQojRo1Kt/+GjVqaNWqVX9uUQAAoEhy+owNAADAvUKwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApkGwAQAApuH0YHP+/HmNGDFCTZo0UZ06dfTcc88pOTnZ1r99+3a1a9dONWvW1OOPP64NGzY4sVoAAODKnB5sBgwYoL1792rKlClas2aNqlWrpu7du+vo0aM6cuSIevXqpccee0xr167V008/rcGDB2v79u3OLhsAALggD2cOfuzYMW3dulUrVqzQI488IkkaPny4vv32W3388cc6e/aswsLC9Oqrr0qSKleurJSUFCUmJqphw4bOLB0AALggpwabgIAALViwQJGRkbY2Nzc3ubm5KSsrS8nJyWrRooXdfRo0aKCxY8fKMAy5ubk5PLaHR+FNVrm7O30iDHBZRf38KOr1A4XJFc4PpwYbPz8/NW3a1K7tiy++0LFjx/SPf/xDH374oUJCQuz6g4KCdOXKFZ07d06lS5d2aFyLxU0BASUcrhuA4/z8iju7BACFxBXOb6cGm9/bs2ePXn/9df31r39Vs2bNlJ2dLS8vL7ttbtzOyclxeByr1VBW1uW7qvWPuLtbXOLJBVxRVtYV5eVZnV2Gwzi/gVsrzPPbz694gWaEXCbYbNy4UYMGDVKdOnWUkJAgSSpWrNhNAebG7eLF7+6FJTe36L6wAkVZXp6V8w8wKVc4v52/GCZp+fLl6tevn5o3b6558+apWLFikqRy5copIyPDbtuMjAz5+PioZMmSzigVAAC4MKcHmxUrVmjMmDHq2LGjpkyZYrf0VLduXe3cudNu+6SkJNWpU0cWi9NLBwAALsapS1GpqakaN26cWrZsqV69eunMmTO2Pm9vb3Xu3FkxMTFKSEhQTEyMtmzZos8//1yJiYlOrBoAALgqpwabL774QteuXdNXX32lr776yq4vJiZGEyZM0Jw5czRp0iS98847uv/++zVp0iQ+wwYAAOTLqcGmd+/e6t279x9u06RJEzVp0uRPqggAABRlXKgCAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMg2ADAABMw6WCzfz589W5c2e7tgMHDqhTp06qVauWoqOjtXTpUidVBwAAXJ3LBJt3331X06ZNs2s7d+6cunbtqgoVKmjNmjWKi4tTQkKC1qxZ45wiAQCAS/NwdgGnTp3SyJEjtWPHDlWsWNGub/Xq1fL09NTo0aPl4eGhypUr69ixY1qwYIFiY2OdUzAAAHBZTg82P/74ozw9PbV+/XrNnj1bJ06csPUlJycrKipKHh7/LbNBgwaaP3++zpw5ozJlyjg8rodH4U1Wubu7zEQY4HKK+vlR1OsHCpMrnB9ODzbR0dGKjo7Oty89PV2hoaF2bUFBQZKktLQ0h4ONxeKmgIASDt0XwN3x8yvu7BIAFBJXOL+dHmz+SHZ2try8vOzaihUrJkm6evWqw/u1Wg1lZV2+q9r+iLu7xSWeXMAVZWVdUV6e1dllOIzzG7i1wjy//fyKF2hGyKWDjbe3t3JycuzabgQaHx+fu9p3bm7RfWEFirK8PCvnH2BSrnB+O38x7A+EhIQoIyPDru3G7eDgYGeUBAAAXJhLB5t69epp9+7dysvLs7UlJSWpUqVKCgwMdGJlAADAFbl0sImNjdWlS5c0bNgwHT58WGvXrtWSJUvUq1cvZ5cGAABckEsHm8DAQCUmJio1NVUxMTGaNWuWBg8erJiYGGeXBgAAXJBLXTw8YcKEm9pq1KihVatWOaEaAABQ1Lj0jA0AAMCdINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTINgAAADTKBLBxmq1asaMGXrsscdUq1Ytvfjii/rll1+cXRYAAHAxRSLYzJkzRytWrNCYMWO0cuVKWa1W9ejRQzk5Oc4uDQAAuBCXDzY5OTlatGiRXn75ZTVr1kxVq1bV1KlTlZ6eri+//NLZ5QEAABfi8sHm4MGD+vXXX9WwYUNbm5+fn6pXr65du3Y5sTIAAOBqPJxdwO2kp6dLksqVK2fXHhQUZOu7UxaLm0qXLnHXtd2Km9v1/w7pHq28PGuhjQMUJe7u1/8/yt+/uAzDycXcBc5v4GZ/xvltsbgVaDuXDzZXrlyRJHl5edm1FytWTBcuXHBon25ubnJ3L9gDdDf8fb0LfQygqLFYXH6iuEA4v4GbucL57fwKbsPb+/qLx+8vFL569aqKFy/ujJIAAICLcvlgc2MJKiMjw649IyNDwcHBzigJAAC4KJcPNlWrVpWvr6927Nhha8vKylJKSorq1avnxMoAAICrcflrbLy8vNSpUyclJCSodOnSKl++vCZNmqSQkBD99a9/dXZ5AADAhbh8sJGkl19+Wbm5uXrjjTeUnZ2tevXqaeHChfL09HR2aQAAwIW4GUZRfuMlAADAf7n8NTYAAAAFRbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbBBkTNu3Di1aNHCru3ixYuqUaOGNm/erD179qhjx46qUaOGmjVrpvj4eF26dMm27b/+9S916NBBtWvXVr169dSvXz+dPHnyzz4MAAUQFhamDz74QC+88IJq1Kihxo0ba9asWXbbbN68Wc8884xq166txo0ba/z48crOznZSxXA2gg2KnHbt2umXX35RcnKyre3TTz+Vn5+fQkJC1LVrVz322GNav369EhIS9OOPP6pbt24yDEN5eXnq1auX6tWrp/Xr12vJkiU6efKk/vGPfzjxiAD8kbfeeksxMTHasGGDOnXqpJkzZ2rXrl2SpK+++kp9+vRRs2bNtHbtWsXHx+vTTz/VgAEDnFw1nKVIfLs38FtVq1ZVeHi41q9fr7p160qSPvzwQ7Vt21YLFy5Uo0aN1Lt3b0lSxYoVNXnyZLVo0UI7d+5U1apVde7cOQUFBal8+fJ64IEHNG3aNJ09e9aZhwTgDzz11FP6+9//Lknq3bu3Fi5cqD179qhevXpasGCBWrZsqb59+0qSKlWqJMMwFBcXp8OHD6tKlSrOLB1OwIwNiqTY2Fh99tlnysnJ0bFjx7R3717FxsYqJSVF33zzjWrXrm37adu2rSTpyJEj8vf3V48ePTRmzBg1bNhQr7zyinbt2qWwsDAnHxGAW6lcubLd7ZIlS+ratWuSpEOHDqlOnTp2/VFRUbY+/O9hxgZF0pNPPqm33npLmzZt0qFDh1SjRg1VrlxZVqtVTz75pG3G5rdKly4tSRo0aJA6dOigLVu2aPv27RozZowSExO1bt06eXl5/dmHAuA28jsvDcOw++9vWa1WSZKHB3/i/hcxY4Miyc/PTy1bttRXX32lL774Qu3atZMkPfzwwzp8+LAefPBB209ubq7Gjx+vtLQ0HT16VCNHjlRgYKCee+45zZgxQ4mJiTpy5IgOHjzo5KMCcKfCwsK0Z88eu7Yb19/9fqYH/xsINiiyYmNj9dVXX+k///mP2rRpI0nq1q2bUlJSFB8fryNHjmjv3r0aOHCgfv75Z1WsWFEBAQHasGGDRowYoSNHjig1NVUffvih/P399dBDDzn5iADcqR49eujLL7/UnDlzlJqaqk2bNmnMmDFq3rw5weZ/FPN0KLIaNmyogIAA1alTR35+fpKkWrVqKTExUdOnT1dMTIx8fHzUsGFDDRkyRF5eXvLy8tLbb7+tyZMn65lnnlFeXp5q1aqlxYsXy9fX18lHBOBOtWrVSlOmTNHcuXM1Z84clS5dWk888YRefvllZ5cGJ3Ez8lugBIqAX3/9VY0bN9bs2bP16KOPOrscAIALYMYGRc6FCxeUlJSkzz77TOXLl1fDhg2dXRIAwEUQbFDk5OXladiwYSpdurSmTZsmNzc3Z5cEAHARLEUBAADT4F1RAADANAg2AADANAg2AADANAg2AADANAg2AAoF70sA4AwEGwD3VE5OjsaNG6ePP/7Y2aXY7NixQ2FhYdqxY4ezSwFQyAg2AO6pjIwMvfPOO8rNzXV2KQD+BxFsAACAaRBsANzk/fffV5s2bRQREaFmzZpp5syZysvLU1pamh555BF17tzZtu3Vq1fVunVrtWnTRkeOHNFf/vIXSdLrr7+u6OhoSdLQoUP1/PPPa+TIkapTp45at26tvLw8ZWZmKj4+Xs2bN1dERISioqIUFxen48eP33HN+/btU7du3VSnTh01aNBAAwYM0KlTp+y2OXr0qLp3766aNWuqUaNGSkhIsJtZslqtWrBggVq2bKmIiAi1atVKy5Ytu2msjRs3ql27doqMjFSjRo305ptv6vLly7b+7OxsjRo1Sk2aNFFERIQef/xxLVy40G4f58+f14gRI/Too48qMjJSzzzzjLZv337Hxw3gdwwA+I158+YZYWFhxpgxY4xvv/3WWLBggREZGWm8/vrrhmEYxurVq43Q0FDjgw8+MAzDMMaOHWuEh4cbBw4cMK5evWp8+eWXRmhoqDF16lTjxx9/NAzDMIYMGWJUr17dePHFF41t27YZGzduNKxWq9G+fXujZcuWxieffGIkJSUZ77zzjlG7dm2jW7dud1Tzjz/+aISHhxsdOnQwvvrqK+Pzzz83WrZsabRp08a4du2akZSUZISGhhqRkZHG7NmzjW3bthljxowxQkNDjWXLltn2M3z4cCM8PNyYMWOG8e233xpTpkwxqlatasyaNcu2zfr1643Q0FBj4MCBxpYtW4wVK1YY9erVM55//nnDarXa9tO8eXPbcU2cONHuMcvOzjbatm1rPProo8bq1auNzZs3G/369TOqV69ubNu2zfEnD4BBsAFgk5WVZdSoUcMYMWKEXfuNMHPo0CHDMAyjR48eRlRUlLFx40YjLCzMSExMtG37yy+/GKGhocaaNWtsbUOGDDFCQ0ONtLQ0W1t6errRuXNnY9euXXZjjRkzxoiIiLijuvv162c0atTIyM7OtrXt2bPHaN68uZGSkmILNpMmTbL1W61Wo2nTpkZcXJxhGIZx9OhRIywszJg/f77dvqdOnWpERkYamZmZhtVqNZo0aWJ0797dbptt27YZoaGhxqZNmwzDMIxWrVoZb7zxht02s2bNsvWvWrXKCA0NNfbt22dXT8eOHY127drd0bEDsMdSFACbvXv3Kjs7W9HR0crNzbX93FhS2rp1qyTpzTfflNVq1UsvvaSoqCh169bttvsuVaqUQkJCbLeDg4O1dOlSPfLIIzp+/Li2bt2qZcuWac+ePcrJybmjunfv3q0mTZqoWLFitrbatWvrn//8p6pVq2Zrq1u3ru13Nzc3lS9fXllZWZKkpKQkGYaR77FfvXpVu3fv1tGjR5Wenn7TNvXq1ZOvr6/t8alfv75Wr16tF198UcuXL9cvv/yiuLg4NWvWTJK0fft2lS1bVuHh4bZ95OXlqXnz5tq/f78uXLhwR8cP4L/4dm8ANufPn5ck9ezZM9/+jIwMSddDScOGDfXFF1+oWbNmBfqG9RIlStzUtn79ek2ZMkVpaWkqVaqUqlWrJm9vb4fqDgwMvO12xYsXt7ttsVhsn7dz49jbtGmT731PnTqlgIAASVJ8fLzi4+Nv2ubG4zNs2DCFhIRo/fr1GjNmjMaMGaPatWtr1KhRqlq1qs6fP6/Tp08rPDw837FOnz4tf3//2x4PgJsRbADY+Pn5SZISEhJUsWLFm/rLlCkjSfruu+/0xRdfqFq1apo5c6ZatmypBx544I7GSk5O1pAhQ9S5c2d1795dwcHBkqSJEydq9+7dd7SvkiVLKjMz86b2LVu22M3Y/JEbx/7OO+/kG8Luu+8+2xiDBw9WVFTUTdvcCCNeXl7q06eP+vTpo5MnT2rTpk2aM2eOBg4cqA0bNqhkyZKqWLGiEhIS8q3l/vvvL1DNAG7GUhQAm5o1a8rT01OnTp1SZGSk7cfDw0NTpkzR8ePHdfHiRb3xxht69NFHtXz5cvn5+ekf//iHbebD3d29QGPt3btXVqtV/fr1s4WavLw8bdu2TdL1dygVVN26dbV161a7JayUlBT17NlTP/74Y4H3IUnnzp2zO/bMzExNnz5d58+f10MPPaTAwEAdP37cbpvg4GBNnjxZKSkpys7OVqtWrbRo0SJJ1wNRx44d1aZNG508eVKSFBUVpbS0NAUGBtrtZ+vWrUpMTCzwYwjgZszYALAJCAhQjx49NH36dF26dEn169fXqVOnNH36dLm5ualq1aoaN26czp07p6VLl8rX11fDhw9XXFycli9frs6dO6tkyZKSrl9HUrlyZdWsWTPfsWrUqCFJGj16tGJjY3XhwgW9++67OnjwoCTp8uXL8vX1LVDdffv21bPPPqtevXqpS5cuys7O1rRp01SjRg01atRIe/fuve0+wsLC1LZtWw0fPlwnTpxQRESEUlNTNXXqVN1///2qWLGi3N3d9eqrr2rEiBFyd3dX8+bNlZWVpTlz5ujUqVMKDw+Xt7e3wsPDNWvWLHl6eiosLEypqan68MMP1apVK0lSu3bttHz5cnXt2lW9e/dWuXLltG3bNr399tvq1KmTPD09C3TcAG7mZhh8oQsAe++++65WrFihY8eOyd/fXw0bNtSAAQP0008/qWfPnnrttdfUo0cP2/b9+vXTt99+q48++kgPPvigJkyYoFWrVsnT01Nbt27V8OHDtXPnTv3zn/+8aZzFixfr1KlTKlOmjOrXr68WLVooLi5OCxYsUNOmTQtc8759+zR58mT961//kq+vr5o2bapBgwapdOnS2rFjh7p06aKlS5eqfv36tvvc+DyeG59Vk5ubq/nz5+vDDz9Uenq6AgMD1bx5c/Xv31+lSpWy3e/TTz9VYmKifvrpJ/n4+KhOnTrq37+/wsLCJEmXLl3StGnT9PXXX+v06dMKDAxU69at9corr9iuITp79qwmT56szZs36+LFiypfvrzat2+vbt26yWJhMh1wFMEGAACYBktRAFxWQb5vymKxMMMBwIZgA8AlHT9+3Pb1DH/kpZdeUr9+/f6EigAUBSxFAXBJOTk5+ve//33b7YKCgmzvqgIAgg0AADANFqYBAIBpEGwAAIBpEGwAAIBpEGwAAIBpEGwAAIBpEGwAAIBpEGwAAIBp/D9ENliQ8El9IwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extra Cheese distribution across the dataset\n",
    "\n",
    "sns.countplot(x = df['extra_cheese'])\n",
    "plt.title('Extra Cheese Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb7e48ee-5290-421b-a058-e1bc7253c778",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0695d62b-ea10-4ca1-bf96-2be41095120e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship Between Price and Company Column\n",
    "\n",
    "sns.barplot(y = df['price'], x = df['company'] )\n",
    "plt.title('Relationship Between Price and Company')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e541cb8a-63a8-4e27-ac28-1dea2083e04a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship Between Price and Topping Column\n",
    "\n",
    "plt.title('Relationship Between Price and Topping')\n",
    "sns.boxplot(x = df['price'], y = df['topping'])\n",
    "plt.xticks(rotation = 65)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0fe7d23d-4018-4e8c-bafd-1ee20a46af31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship Between Price and Size Column\n",
    "\n",
    "plt.title('Relationship Between Price and Size')\n",
    "sns.boxplot(y = df['price'], x = df['size'])\n",
    "plt.xticks(rotation = 65)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0eee0deb-c5ce-4601-a591-9e6626e77046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>1339.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>mozzarella</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company   price  diameter     topping           variant   size extra_sauce  \\\n",
       "4       A  1339.2      18.0  mozzarella  double_signature  jumbo         yes   \n",
       "\n",
       "  extra_cheese extra_mushrooms  \n",
       "4           no             yes  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the most expensive pizza\n",
    "\n",
    "df[df['price'].max()==df['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4f2a5410-9f03-405f-8709-9c54a2b21707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     22.0\n",
       "1     20.0\n",
       "4     18.0\n",
       "5     18.5\n",
       "6     16.0\n",
       "10    18.5\n",
       "11    16.0\n",
       "15    18.5\n",
       "16    16.0\n",
       "20    22.0\n",
       "21    18.5\n",
       "79    18.5\n",
       "80    16.0\n",
       "84    22.0\n",
       "85    18.5\n",
       "Name: diameter, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the diameter of a jumbo size pizza\n",
    "\n",
    "df[df['size']=='jumbo']['diameter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc7643ec-f714-4917-a9dc-e327a3922baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29     17.0\n",
       "35     17.0\n",
       "41     17.0\n",
       "47     17.0\n",
       "93     17.0\n",
       "99     17.0\n",
       "104    17.0\n",
       "116    17.0\n",
       "Name: diameter, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the diameter of XL size pizzas\n",
    "\n",
    "df[df['size']=='XL']['diameter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a591fdd-5c27-4ddc-8c8d-69cd16ee7dd4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5f3f2332-b478-4606-8c3c-d9c5bb90b748",
   "metadata": {},
   "source": [
    "## Ouliers Removal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ae9d3a-f37b-4514-ae8b-0a29b080aacc",
   "metadata": {},
   "source": [
    "XL size pizza is 17inch and why will a jumbo size pizza have a diameter les than 17inch. That means that jumbo size pizza with diameter leass than 17inch are outliers in the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4d5fbe43-dea4-4499-8677-bedfccab0443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A</td>\n",
       "      <td>594.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>smoked_beef</td>\n",
       "      <td>american_favorite</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A</td>\n",
       "      <td>594.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>black_papper</td>\n",
       "      <td>super_supreme</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A</td>\n",
       "      <td>594.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>smoked_beef</td>\n",
       "      <td>meat_lovers</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>D</td>\n",
       "      <td>594.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>smoked_beef</td>\n",
       "      <td>meat_lovers</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   company  price  diameter       topping            variant   size  \\\n",
       "6        A  594.0      16.0   smoked_beef  american_favorite  jumbo   \n",
       "11       A  594.0      16.0  black_papper      super_supreme  jumbo   \n",
       "16       A  594.0      16.0   smoked_beef        meat_lovers  jumbo   \n",
       "80       D  594.0      16.0   smoked_beef        meat_lovers  jumbo   \n",
       "\n",
       "   extra_sauce extra_cheese extra_mushrooms  \n",
       "6           no          yes             yes  \n",
       "11         yes          yes              no  \n",
       "16          no          yes             yes  \n",
       "80          no          yes              no  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing Jumbo size pizza outliers\n",
    "\n",
    "df[(df['size']=='jumbo') & (df['diameter']<=16)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "408ab70f-b728-4404-bdf6-d58a62c6d73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df.index[[6,11,16,80]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d23dc63-48af-46a6-b097-2cf499e68a3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "607a9ffe-d0c7-4d81-960e-deb596650c18",
   "metadata": {},
   "source": [
    "## Feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3ec35639-48dd-40a1-be8c-e04c01327f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['company', 'topping', 'variant', 'size', 'extra_sauce', 'extra_cheese',\n",
       "       'extra_mushrooms'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtaining and defining categorical columns in the dataset\n",
    "\n",
    "cate_cols = df.select_dtypes(include=['object']).columns\n",
    "cate_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dc8df9e2-5b85-4a81-bb0b-3b6c305a619f",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "for i in cate_cols:\n",
    "    df[i] = le.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "77cad6f9-550a-4155-a71b-3c38c751e99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1069.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1339.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   company   price  diameter  topping  variant  size  extra_sauce  \\\n",
       "0        0  1269.0      22.0        2        8     1            1   \n",
       "1        0  1069.2      20.0        7        8     1            1   \n",
       "2        0   648.0      16.0        5        8     4            1   \n",
       "3        0   837.0      14.0        9        8     4            1   \n",
       "4        0  1339.2      18.0        4        8     1            1   \n",
       "\n",
       "   extra_cheese  extra_mushrooms  \n",
       "0             1                0  \n",
       "1             1                0  \n",
       "2             1                1  \n",
       "3             0                1  \n",
       "4             0                1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "75d9abf3-0020-46b2-a3ab-5f3a2b10a6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('price', axis = 1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329a717e-e198-45f0-89ef-54b8d377fc7e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "621d2db1-de8e-4951-b05b-0a868cff67f6",
   "metadata": {},
   "source": [
    "## Model Development"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03a6114-b53e-4916-8d1d-cc47ec5b7ff9",
   "metadata": {},
   "source": [
    "Here, I have used Logistic regression, Support Vector Machine, Random forest and Gradient boosting models to predict churn customers and compared which model works better for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8209db5b-4f57-42a5-843f-4377f27e440d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test= train_test_split(X,y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0367f5d0-4796-45b0-ba63-32f78e7771c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "09fa7b66-f676-47a7-a047-4bf09e69c3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "svm = SVR()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "rfr = RandomForestRegressor()\n",
    "rfr.fit(X_train, y_train)\n",
    "\n",
    "gb = GradientBoostingRegressor()\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "xgb = XGBRegressor()\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c8550cfb-e675-4ae2-866c-424c9ab7941c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = lr.predict(X_test)\n",
    "y_pred2 = svm.predict(X_test)\n",
    "y_pred3 = rfr.predict(X_test)\n",
    "y_pred4 = gb.predict(X_test)\n",
    "y_pred5 = xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9172f69b-eb7b-4cd1-ba04-04f4f87c296b",
   "metadata": {},
   "source": [
    "## Model Eveluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "762072fb-ca46-4a88-9fe5-7257930a3b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "score1 = r2_score(y_test, y_pred1)\n",
    "score2 = r2_score(y_test, y_pred2)\n",
    "score3 = r2_score(y_test, y_pred3)\n",
    "score4 = r2_score(y_test, y_pred4)\n",
    "score5 = r2_score(y_test, y_pred5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1a83b27b-4d14-4c82-adb1-97771b35f74f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6391199071772933 -0.05977151402032166 0.7215242536813213 0.8160443660009487 0.7305652447272124\n"
     ]
    }
   ],
   "source": [
    "print(score1, score2, score3, score4, score5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "16d53f8d-f8b4-4423-ba23-6c46bb872ef9",
   "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>Model</th>\n",
       "      <th>R2_SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.639120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>-0.059772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RFR</td>\n",
       "      <td>0.721524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GB</td>\n",
       "      <td>0.816044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGB</td>\n",
       "      <td>0.730565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model  R2_SCORE\n",
       "0    LR  0.639120\n",
       "1   SVM -0.059772\n",
       "2   RFR  0.721524\n",
       "3    GB  0.816044\n",
       "4   XGB  0.730565"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = pd.DataFrame({'Model':['LR', 'SVM','RFR','GB', 'XGB'],\n",
    "                          'R2_SCORE': [r2_score(y_test, y_pred1),\n",
    "                                  r2_score(y_test, y_pred2),\n",
    "                                  r2_score(y_test, y_pred3),\n",
    "                                  r2_score(y_test, y_pred4),\n",
    "                                  r2_score(y_test, y_pred5),\n",
    "                                 ]})\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "13e4ae50-57d2-4169-bc24-f645adc162d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Model', ylabel='R2_SCORE'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y = final_data.R2_SCORE, x = final_data.Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3807363-53cf-45e4-96d0-0ac4b96a14de",
   "metadata": {},
   "source": [
    "The Gradient Boost Regressor model is the best model for the prediction with an R2_Score of 81.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc61d926-38a7-4628-8c03-e86cdf730151",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfd0e17-c37c-4575-a7d9-82f75a941e7b",
   "metadata": {},
   "source": [
    "Let get to know the features that influnence the Gradient Boosting and XGBoosting Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a0d3ce7e-ca4d-407d-8260-1f33fe2f254c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company            0.006979\n",
       "diameter           0.653605\n",
       "topping            0.046912\n",
       "variant            0.110409\n",
       "size               0.151964\n",
       "extra_sauce        0.023574\n",
       "extra_cheese       0.003653\n",
       "extra_mushrooms    0.002904\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient Boosting Regressor\n",
    "\n",
    "gb.feature_importances_\n",
    "fea2 = pd.Series(gb.feature_importances_, index = X_train.columns)\n",
    "fea2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4a6c5a7e-4564-4ea0-ae52-49e0bb6b775f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 42,
     "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": [
    "fea2.plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a43915-fc43-46e1-8e5d-f725c07bac36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e230c714-9c5b-4759-9bc9-1b84fe6b331d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company            0.004064\n",
       "diameter           0.434312\n",
       "topping            0.009284\n",
       "variant            0.069684\n",
       "size               0.417542\n",
       "extra_sauce        0.052759\n",
       "extra_cheese       0.009080\n",
       "extra_mushrooms    0.003276\n",
       "dtype: float32"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#XGBoosting Regressor\n",
    "\n",
    "xgb.feature_importances_\n",
    "fea3 = pd.Series(xgb.feature_importances_, index = X_train.columns)\n",
    "fea3  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "65bb94fd-6f5f-439e-b333-1c396772b879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 44,
     "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": [
    "fea3.plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d34c69c-a5e3-4f45-b85e-bf1073157daa",
   "metadata": {},
   "source": [
    "Through out the models, the top 3 features; Size, Varient and Diameter features, shows to be important in the prediction of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc5af682-89fa-4906-9546-40b0cc19e776",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ff30a24c-c8c2-4217-91df-9ba3ee3b83d6",
   "metadata": {},
   "source": [
    "## Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "71fd637e-58ac-4789-9580-22c67fa7ee09",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = GradientBoostingRegressor()\n",
    "gb.fit(X_train, y_train)\n",
    "y_pred = gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0517dfaa-673c-47ff-abb2-18d7d1e4a9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2837cb6b-873b-4c92-8295-d1975c075aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"Pizza_price_prediction.pki\", \"wb\") as f:\n",
    "    pickle.dump(gb,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2797d86-d082-41e4-a14a-74efb4832c3a",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6436fad-785f-4bdd-8fa8-d1d0770b1f10",
   "metadata": {},
   "source": [
    "In this notebook, I deploy machine learning models to predict the pizza price  with prediction score of 81.5%. \n",
    "\n",
    "Determined the 3 main/key features influnecing the price, which are diameter, size and varient of the pizza.\n",
    "\n",
    "This analysis would help the sales departement determine the best prices for new launching pizza menus and keeping profit loss of the company on the first year below 20%.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e13d9df7-014d-4153-8688-e3f150fa44b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}