Encoding 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67092f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f424a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day26-ordinal-encoding/customer.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df4d284e",
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>review</th>\n",
       "      <th>education</th>\n",
       "      <th>purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>Female</td>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>Female</td>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>Female</td>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>Female</td>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>Female</td>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender   review education purchased\n",
       "0   30  Female  Average    School        No\n",
       "1   68  Female     Poor        UG        No\n",
       "2   70  Female     Good        PG        No\n",
       "3   72  Female     Good        PG        No\n",
       "4   16  Female  Average        UG        No"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa3cd207",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1fdb2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>education</th>\n",
       "      <th>purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    review education purchased\n",
       "0  Average    School        No\n",
       "1     Poor        UG        No\n",
       "2     Good        PG        No\n",
       "3     Good        PG        No\n",
       "4  Average        UG        No"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "039318e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>review</th>\n",
       "      <th>education</th>\n",
       "      <th>purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Average</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     review education purchased\n",
       "5   Average    School       Yes\n",
       "1      Poor        UG        No\n",
       "44  Average        UG        No\n",
       "21  Average        PG        No\n",
       "0   Average    School        No"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92ae1755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['School', 'UG', 'PG'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.education.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c0b3ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(df.iloc[:,:-1],df.iloc[:,-1],test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b41be308",
   "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>review</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Good</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Good</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Poor</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review education\n",
       "23   Good    School\n",
       "18   Good    School\n",
       "49   Good        UG\n",
       "7    Poor    School\n",
       "16   Poor        UG"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf7802e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "efa00ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "oe=OrdinalEncoder(categories=[[\"Poor\",\"Average\",\"Good\"],[\"School\",\"PG\",\"UG\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "73bd836d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=oe.fit_transform(x_train)\n",
    "x_test=oe.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56ca8af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2.],\n",
       "       [2., 1.],\n",
       "       [0., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [0., 2.],\n",
       "       [2., 1.],\n",
       "       [1., 2.],\n",
       "       [2., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 2.],\n",
       "       [1., 2.],\n",
       "       [0., 1.],\n",
       "       [2., 2.],\n",
       "       [1., 0.],\n",
       "       [2., 2.],\n",
       "       [2., 0.],\n",
       "       [2., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [2., 0.],\n",
       "       [1., 1.],\n",
       "       [2., 2.],\n",
       "       [2., 1.],\n",
       "       [2., 2.],\n",
       "       [1., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 1.],\n",
       "       [2., 2.],\n",
       "       [0., 2.],\n",
       "       [1., 2.],\n",
       "       [0., 0.],\n",
       "       [1., 2.],\n",
       "       [2., 0.],\n",
       "       [2., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 2.],\n",
       "       [0., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e67da5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['Poor', 'Average', 'Good'], dtype=object),\n",
       " array(['School', 'PG', 'UG'], dtype=object)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oe.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85f947df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e710e599",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a0a0b02a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.fit(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4bb68f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2e97007b",
   "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>review</th>\n",
       "      <th>education</th>\n",
       "      <th>purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    review education purchased\n",
       "0  Average    School        No\n",
       "1     Poor        UG        No\n",
       "2     Good        PG        No\n",
       "3     Good        PG        No\n",
       "4  Average        UG        No"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4053253d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_train=le.fit_transform(y_train)\n",
    "# y_test=le.transform(y_test)\n",
    "y_train = le.transform(y_train)\n",
    "y_test = le.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fbef6cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3c31ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "l=[\"a\",\"b\",\"c\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d358105a",
   "metadata": {},
   "outputs": [],
   "source": [
    "l[0]=\"D\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5dca645",
   "metadata": {},
   "outputs": [],
   "source": [
    "n=[1,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8614387b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4af8f128",
   "metadata": {},
   "outputs": [],
   "source": [
    "p=[[2,4],\n",
    "   [4,5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73c882e7",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ABDURR~1\\AppData\\Local\\Temp/ipykernel_1228/2901213227.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'dtype'"
     ]
    }
   ],
   "source": [
    "d=[]\n",
    "for i in p:\n",
    "    d.append()"
   ]
  },
  {
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   "execution_count": null,
   "id": "53f7a7bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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