SKLearn 09, Label Encoding & One Hot Encoding.ipynb

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   "cell_type": "markdown",
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   "source": [
    "#  Mengenal Categorical Encoding:\n",
    "# Label Encoding & One Hot Encoding\n",
    "\n",
    "### Apa itu Categorical Encoding?\n",
    "\n",
    "Categorical Encoding adalah proses konversi nilai categorical menjadi nilai numerical.\n",
    "\n",
    "Terdapat banyak jenis Categorical Encoding, dua di antaranya adalah:\n",
    "\n",
    "- Label Encoding\n",
    "- One Hot Encoding\n",
    "Referensi: https://en.wikipedia.org/wiki/One-hot\n",
    "\n",
    "### Label Encoding\n",
    "Pada Label Encoding, setiap kategori pada suatu feature akan diurutkan secara alfabet dan direpresentasikan dengan sebuah nilai integer.\n",
    "\n",
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>34</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>46</td>\n",
       "      <td>98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>35</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>23</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  age  salary\n",
       "0   India   44   72000\n",
       "1      US   34   65000\n",
       "2   Japan   46   98000\n",
       "3      US   35   45000\n",
       "4   Japan   23   34000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'country': ['India', 'US', 'Japan', 'US', 'Japan'],\n",
    "    'age': [44, 34, 46, 35, 23],\n",
    "    'salary': [72000, 65000, 98000, 45000, 34000]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding pada Scikit Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>country</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  age  salary\n",
       "0        0   44   72000\n",
       "1        2   34   65000\n",
       "2        1   46   98000\n",
       "3        2   35   45000\n",
       "4        1   23   34000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "df['country'] = label_encoder.fit_transform(df['country'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['India', 'Japan', 'US'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding\n",
    "Pada One Hot Encoding, setiap kategori pada suatu feature akan diurutkan secara alfabet dan direpresentasikan sebagai sekumpulan bits.\n",
    "\n",
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>country</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>34</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>46</td>\n",
       "      <td>98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>35</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>23</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  age  salary\n",
       "0   India   44   72000\n",
       "1      US   34   65000\n",
       "2   Japan   46   98000\n",
       "3      US   35   45000\n",
       "4   Japan   23   34000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'country': ['India', 'US', 'Japan', 'US', 'Japan'],\n",
    "    'age': [44, 34, 46, 35, 23],\n",
    "    'salary': [72000, 65000, 98000, 45000, 34000]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding pada Scikit Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['India'],\n",
       "       ['US'],\n",
       "       ['Japan'],\n",
       "       ['US'],\n",
       "       ['Japan']], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df['country'].values.reshape(-1,1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "onehot_encoder = OneHotEncoder()\n",
    "X = onehot_encoder.fit_transform(X).toarray()\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['India', 'Japan', 'US'], dtype=object)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     0    1    2\n",
       "0  1.0  0.0  0.0\n",
       "1  0.0  0.0  1.0\n",
       "2  0.0  1.0  0.0\n",
       "3  0.0  0.0  1.0\n",
       "4  0.0  1.0  0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "df_onehot = pd.DataFrame(X, columns=[str(i) for i in range(X.shape[1])])\n",
    "df_onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>India</td>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>US</td>\n",
       "      <td>34</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>46</td>\n",
       "      <td>98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>US</td>\n",
       "      <td>35</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>23</td>\n",
       "      <td>34000</td>\n",
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      "text/plain": [
       "     0    1    2 country  age  salary\n",
       "0  1.0  0.0  0.0   India   44   72000\n",
       "1  0.0  0.0  1.0      US   34   65000\n",
       "2  0.0  1.0  0.0   Japan   46   98000\n",
       "3  0.0  0.0  1.0      US   35   45000\n",
       "4  0.0  1.0  0.0   Japan   23   34000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df_onehot, df], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2  age  salary\n",
       "0  1.0  0.0  0.0   44   72000\n",
       "1  0.0  0.0  1.0   34   65000\n",
       "2  0.0  1.0  0.0   46   98000\n",
       "3  0.0  0.0  1.0   35   45000\n",
       "4  0.0  1.0  0.0   23   34000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['country'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label Encoding vs One Hot Encoding\n",
    "Kita menerapkan One Hot Encoding bila:\n",
    "\n",
    "- Nilai categorical adalah nominal\n",
    "- Jumlah kategori yang ada tidak terlalu banyak\n",
    "\n",
    "Kita menerapkan Label Encoding bila:\n",
    "\n",
    "- Nilai categorical adalah ordinal\n",
    "- Jumlah kategori yang ada relatif banyak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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