19 Encoding1_Jun25.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "36449c69-4353-4971-8ac1-478c9b8ca227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset: Social_Network_Ads.csv\n",
    "# location: https://mitu.co.in/dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6c3dd3c-13e1-4e63-8ff1-ce071ab81807",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecb87cea-5031-47f4-a67a-916bc1ae64cc",
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>76000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>15691863</td>\n",
       "      <td>Female</td>\n",
       "      <td>46.0</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15706071</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>15654296</td>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>Male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender   Age  EstimatedSalary  Purchased\n",
       "0    15624510    Male  19.0          19000.0          0\n",
       "1    15810944    Male  35.0          20000.0          0\n",
       "2    15668575  Female  26.0          43000.0          0\n",
       "3    15603246  Female  27.0          57000.0          0\n",
       "4    15804002    Male  19.0          76000.0          0\n",
       "..        ...     ...   ...              ...        ...\n",
       "395  15691863  Female  46.0          41000.0          1\n",
       "396  15706071    Male  51.0          23000.0          1\n",
       "397  15654296  Female  50.0          20000.0          1\n",
       "398  15755018    Male  36.0          33000.0          0\n",
       "399  15594041  Female  49.0          36000.0          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Social_Network_Ads.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cdb37d-d86c-46d4-b2e5-68c4371f0a55",
   "metadata": {},
   "source": [
    "## Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "286d745b-b2a1-4206-8e55-ab417d1172d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa65328a-077d-4760-874d-7e11bc5743d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ea26aed-30ae-4632-8725-b109611a728b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "900e526b-e419-433f-81c0-d6ae627db99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    204\n",
       "Male      196\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7fca6d40-04ce-46ae-8549-94a262879219",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b27898b5-48f9-4ce5-8857-8619acc542b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "52681f17-ed4f-4452-bb29-b189f3fe4cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf['Gender'] = le.fit_transform(newdf['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f0240d7c-65db-4a36-9012-368a3f372fb5",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>27.0</td>\n",
       "      <td>57000.0</td>\n",
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       "      <td>46.0</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15706071</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>15654296</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender   Age  EstimatedSalary  Purchased\n",
       "0    15624510       1  19.0          19000.0          0\n",
       "1    15810944       1  35.0          20000.0          0\n",
       "2    15668575       0  26.0          43000.0          0\n",
       "3    15603246       0  27.0          57000.0          0\n",
       "4    15804002       1  19.0          76000.0          0\n",
       "..        ...     ...   ...              ...        ...\n",
       "395  15691863       0  46.0          41000.0          1\n",
       "396  15706071       1  51.0          23000.0          1\n",
       "397  15654296       0  50.0          20000.0          1\n",
       "398  15755018       1  36.0          33000.0          0\n",
       "399  15594041       0  49.0          36000.0          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f0a10df-1400-4f7d-8491-acdd0426b013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "0    204\n",
       "1    196\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe83e9f-e833-4dba-94f9-4f798a3f622e",
   "metadata": {},
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2babe316-b838-4049-8855-cb8b4cd166c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "277c3907-c2ba-4cc3-9400-ab3b126e0278",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
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       "      <td>True</td>\n",
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       "      <td>15654296</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID   Age  EstimatedSalary  Purchased  Gender_Female  Gender_Male\n",
       "0    15624510  19.0          19000.0          0          False         True\n",
       "1    15810944  35.0          20000.0          0          False         True\n",
       "2    15668575  26.0          43000.0          0           True        False\n",
       "3    15603246  27.0          57000.0          0           True        False\n",
       "4    15804002  19.0          76000.0          0          False         True\n",
       "..        ...   ...              ...        ...            ...          ...\n",
       "395  15691863  46.0          41000.0          1           True        False\n",
       "396  15706071  51.0          23000.0          1          False         True\n",
       "397  15654296  50.0          20000.0          1           True        False\n",
       "398  15755018  36.0          33000.0          0          False         True\n",
       "399  15594041  49.0          36000.0          1           True        False\n",
       "\n",
       "[400 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8d7a9ae-a021-4682-adc6-784d6a6b9654",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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