Decision Tree.ipynb

{
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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>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary_more_than_100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelers</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelers</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  company                  job     degree  salary_more_than_100k\n",
       "0  google      sales executive  bachelers                      0\n",
       "1  google      sales executive    masters                      0\n",
       "2  google     business manager  bachelers                      1\n",
       "3  google     business manager    masters                      1\n",
       "4  google  computer programmer  bachelers                      0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('salaries.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f25ad81b",
   "metadata": {},
   "outputs": [
    {
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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>job</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  company                  job     degree\n",
       "0  google      sales executive  bachelers\n",
       "1  google      sales executive    masters\n",
       "2  google     business manager  bachelers\n",
       "3  google     business manager    masters\n",
       "4  google  computer programmer  bachelers"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = df.drop(['salary_more_than_100k'], axis = 'columns')\n",
    "i.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9fdd1f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder(drop = 'first', sparse = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf9f3648",
   "metadata": {},
   "outputs": [],
   "source": [
    "i_n = ohe.fit_transform(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aeb64b53",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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       "       [1., 0., 1., 0., 1.]])"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17e61795",
   "metadata": {},
   "outputs": [],
   "source": [
    "i_n_n = pd.DataFrame(i_n, columns = ['facebook', 'google', 'c_progr', 'sales_ex', 'masters'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1a99315",
   "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>facebook</th>\n",
       "      <th>google</th>\n",
       "      <th>c_progr</th>\n",
       "      <th>sales_ex</th>\n",
       "      <th>masters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    facebook  google  c_progr  sales_ex  masters\n",
       "0        0.0     1.0      0.0       1.0      0.0\n",
       "1        0.0     1.0      0.0       1.0      1.0\n",
       "2        0.0     1.0      0.0       0.0      0.0\n",
       "3        0.0     1.0      0.0       0.0      1.0\n",
       "4        0.0     1.0      1.0       0.0      0.0\n",
       "5        0.0     1.0      1.0       0.0      1.0\n",
       "6        0.0     0.0      0.0       1.0      1.0\n",
       "7        0.0     0.0      1.0       0.0      0.0\n",
       "8        0.0     0.0      0.0       0.0      0.0\n",
       "9        0.0     0.0      0.0       0.0      1.0\n",
       "10       1.0     0.0      0.0       1.0      0.0\n",
       "11       1.0     0.0      0.0       1.0      1.0\n",
       "12       1.0     0.0      0.0       0.0      0.0\n",
       "13       1.0     0.0      0.0       0.0      1.0\n",
       "14       1.0     0.0      1.0       0.0      0.0\n",
       "15       1.0     0.0      1.0       0.0      1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_n_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45c80c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "o = df['salary_more_than_100k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa3a99ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     1\n",
       "3     1\n",
       "4     0\n",
       "5     1\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     1\n",
       "10    1\n",
       "11    1\n",
       "12    1\n",
       "13    1\n",
       "14    1\n",
       "15    1\n",
       "Name: salary_more_than_100k, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4b7f571",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8da6008d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mymodel = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d0ff10d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f51e077",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(i_n_n, o, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5e6ad132",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   "id": "5131d960",
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      ]
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     "execution_count": 15,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 16,
   "id": "8fb76b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>facebook</th>\n",
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       "   facebook  google  c_progr  sales_ex  masters\n",
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     "text": [
      "C:\\Python 310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
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     "execution_count": 17,
     "metadata": {},
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   "execution_count": 18,
   "id": "51d398eb",
   "metadata": {},
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     "text": [
      "C:\\Python 310\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
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     "data": {
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       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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   "execution_count": null,
   "id": "25a60eca",
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