One_Hot_Encoding.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "One Hot Encoding(OHE)\n",
    "One hot encoding, also known as nominal encoding, is a technique used to represent categorical data as numerical data, which is more suitable for machine learning algorithms. In this technique, each category is represented as a binary vector where each bit corresponds to a unique category. For example, if we have a categorical variable \"color\" with three possible values (red, green, blue), we can represent it using one hot encoding as follows:\n",
    "\n",
    "Red: [1, 0, 0]\n",
    "Green: [0, 1, 0]\n",
    "Blue: [0, 0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "761a4e18-9d54-4200-98fd-db4a44af0e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "54e07ec4-d572-4be7-9186-8150ead5a91d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "03a7b37c-9774-47f6-a4de-2e9425058971",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a simple dataframe\n",
    "df = pd.DataFrame({\n",
    "    'color':['red','green','blue','green','red']\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "86bc5f25-4903-4ef2-a949-96cc5eeeb2f6",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red</td>\n",
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      "text/plain": [
       "   color\n",
       "0    red\n",
       "1  green\n",
       "2   blue\n",
       "3  green\n",
       "4    red"
      ]
     },
     "execution_count": 26,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fc3978a2-5b51-4184-adae-79a48f66bb5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create an instance of Onehotencoder\n",
    "encoder = OneHotEncoder()\n",
    "## perform fit and transform\n",
    "encoded = encoder.fit_transform(df[['color']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7ed33ead-d3aa-4351-969b-157c71fea38c",
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   "outputs": [
    {
     "data": {
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       "array([[0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
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     "execution_count": 73,
     "metadata": {},
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   "source": [
    "encoded"
   ]
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  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7afac364-f712-4787-9278-6e41c19a2878",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "encoder_df=pd.DataFrame(encoded,columns=encoder.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "48f146fd-0b8f-44a2-a46d-2beddd72b560",
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      "text/plain": [
       "   color_blue  color_green  color_red\n",
       "0         0.0          0.0        1.0\n",
       "1         0.0          1.0        0.0\n",
       "2         1.0          0.0        0.0\n",
       "3         0.0          1.0        0.0\n",
       "4         0.0          0.0        1.0"
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    "encoder_df"
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   "cell_type": "code",
   "execution_count": 76,
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       "   color  color_blue  color_green  color_red\n",
       "0    red         0.0          0.0        1.0\n",
       "1  green         0.0          1.0        0.0\n",
       "2   blue         1.0          0.0        0.0\n",
       "3  green         0.0          1.0        0.0\n",
       "4    red         0.0          0.0        1.0"
      ]
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   "source": [
    "pd.concat([df,encoder_df],axis = 1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f66d48d6-9757-4e88-b78e-40111ffe7d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Perfom one hot encoding on the dataset 'tips' by using all its categorical features.\n",
    "df = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3ddbc9d9-7d05-4144-adc8-afd058c2813f",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
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       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 83,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "44ccfb16-54b2-4845-a256-cbc53a24d8bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "hot = OneHotEncoder()\n",
    "hot_encoded_data = encoder.fit_transform(df[['sex', 'smoker', 'day', 'time']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eeecd0f2-f879-40ba-a23a-40f4bfe531fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "hot_encoded_df = pd.DataFrame(hot_encoded_data.toarray(), columns=encoder.get_feature_names_out(['sex', 'smoker', 'day', 'time']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7143beaf-6c42-4410-badd-7d829c803769",
   "metadata": {},
   "outputs": [
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       "      <th>smoker_No</th>\n",
       "      <th>smoker_Yes</th>\n",
       "      <th>day_Fri</th>\n",
       "      <th>day_Sat</th>\n",
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       "      <th>time_Dinner</th>\n",
       "      <th>time_Lunch</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sex_Female  sex_Male  smoker_No  smoker_Yes  day_Fri  day_Sat  day_Sun  \\\n",
       "0           1.0       0.0        1.0         0.0      0.0      0.0      1.0   \n",
       "1           0.0       1.0        1.0         0.0      0.0      0.0      1.0   \n",
       "2           0.0       1.0        1.0         0.0      0.0      0.0      1.0   \n",
       "3           0.0       1.0        1.0         0.0      0.0      0.0      1.0   \n",
       "4           1.0       0.0        1.0         0.0      0.0      0.0      1.0   \n",
       "..          ...       ...        ...         ...      ...      ...      ...   \n",
       "239         0.0       1.0        1.0         0.0      0.0      1.0      0.0   \n",
       "240         1.0       0.0        0.0         1.0      0.0      1.0      0.0   \n",
       "241         0.0       1.0        0.0         1.0      0.0      1.0      0.0   \n",
       "242         0.0       1.0        1.0         0.0      0.0      1.0      0.0   \n",
       "243         1.0       0.0        1.0         0.0      0.0      0.0      0.0   \n",
       "\n",
       "     day_Thur  time_Dinner  time_Lunch  \n",
       "0         0.0          1.0         0.0  \n",
       "1         0.0          1.0         0.0  \n",
       "2         0.0          1.0         0.0  \n",
       "3         0.0          1.0         0.0  \n",
       "4         0.0          1.0         0.0  \n",
       "..        ...          ...         ...  \n",
       "239       0.0          1.0         0.0  \n",
       "240       0.0          1.0         0.0  \n",
       "241       0.0          1.0         0.0  \n",
       "242       0.0          1.0         0.0  \n",
       "243       1.0          1.0         0.0  \n",
       "\n",
       "[244 rows x 10 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hot_encoded_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "192fbf30-5c0a-4be2-b5d7-0101a20b2a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  }
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