Feature_Construction_OR_Feature_Splitting.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')[['Age','Pclass','SibSp','Parch','Survived']]"
   ]
  },
  {
   "cell_type": "code",
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       "      <th>Pclass</th>\n",
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       "    Age  Pclass  SibSp  Parch  Survived\n",
       "0  22.0       3      1      0         0\n",
       "1  38.0       1      1      0         1\n",
       "2  26.0       3      0      0         1\n",
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     "execution_count": 87,
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   "source": [
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
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       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "    Age  Pclass  SibSp  Parch  Survived\n",
       "0  22.0       3      1      0         0\n",
       "1  38.0       1      1      0         1\n",
       "2  26.0       3      0      0         1\n",
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     "execution_count": 89,
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:4]\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
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       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "    Age  Pclass  SibSp  Parch\n",
       "0  22.0       3      1      0\n",
       "1  38.0       1      1      0\n",
       "2  26.0       3      0      0\n",
       "3  35.0       1      1      0\n",
       "4  35.0       3      0      0"
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    "X.head()"
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   "cell_type": "code",
   "execution_count": 92,
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    {
     "data": {
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       "0.6933333333333332"
      ]
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     "execution_count": 92,
     "metadata": {},
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   "source": [
    "np.mean(cross_val_score(LogisticRegression(),X,y,scoring='accuracy',cv=20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Feature Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['Family_size'] = X['SibSp'] + X['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
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   "outputs": [
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       "    Age  Pclass  SibSp  Parch  Family_size\n",
       "0  22.0       3      1      0            2\n",
       "1  38.0       1      1      0            2\n",
       "2  26.0       3      0      0            1\n",
       "3  35.0       1      1      0            2\n",
       "4  35.0       3      0      0            1"
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     "execution_count": 94,
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    "X.head()"
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   "cell_type": "code",
   "execution_count": 61,
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   "outputs": [],
   "source": [
    "def myfunc(num):\n",
    "    if num == 1:\n",
    "        #alone\n",
    "        return 0\n",
    "    elif num >1 and num <=4:\n",
    "        # small family\n",
    "        return 1\n",
    "    else:\n",
    "        # large family\n",
    "        return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
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       "1"
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     "execution_count": 62,
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   "source": [
    "myfunc(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['Family_type'] = X['Family_size'].apply(myfunc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
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       "    Age  Pclass  SibSp  Parch  Family_size  Family_type\n",
       "0  22.0       3      1      0            2            1\n",
       "1  38.0       1      1      0            2            1\n",
       "2  26.0       3      0      0            1            0\n",
       "3  35.0       1      1      0            2            1\n",
       "4  35.0       3      0      0            1            0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
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    "X.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(columns=['SibSp','Parch','Family_size'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
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       "    Age  Pclass  Family_type\n",
       "0  22.0       3            1\n",
       "1  38.0       1            1\n",
       "2  26.0       3            0\n",
       "3  35.0       1            1\n",
       "4  35.0       3            0"
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     "execution_count": 98,
     "metadata": {},
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    "X.head()"
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   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
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     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "np.mean(cross_val_score(LogisticRegression(),X,y,scoring='accuracy',cv=20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund, Mr. Owen Harris\n",
       "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                                 Heikkinen, Miss. Laina\n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "886                                Montvila, Rev. Juozas\n",
       "887                         Graham, Miss. Margaret Edith\n",
       "888             Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "889                                Behr, Mr. Karl Howell\n",
       "890                                  Dooley, Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Title'] = df['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Mr\n",
       "1       Mrs\n",
       "2      Miss\n",
       "3       Mrs\n",
       "4        Mr\n",
       "       ... \n",
       "886     Rev\n",
       "887    Miss\n",
       "888    Miss\n",
       "889      Mr\n",
       "890      Mr\n",
       "Name: 0, Length: 891, dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mr</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miss</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mr</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>Rev</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Miss</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Miss</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
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       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <th>890</th>\n",
       "      <td>Mr</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title                                               Name\n",
       "0      Mr                            Braund, Mr. Owen Harris\n",
       "1     Mrs  Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2    Miss                             Heikkinen, Miss. Laina\n",
       "3     Mrs       Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4      Mr                           Allen, Mr. William Henry\n",
       "..    ...                                                ...\n",
       "886   Rev                              Montvila, Rev. Juozas\n",
       "887  Miss                       Graham, Miss. Margaret Edith\n",
       "888  Miss           Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "889    Mr                              Behr, Mr. Karl Howell\n",
       "890    Mr                                Dooley, Mr. Patrick\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Title','Name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "the Countess    1.000000\n",
       "Mlle            1.000000\n",
       "Lady            1.000000\n",
       "Ms              1.000000\n",
       "Sir             1.000000\n",
       "Mme             1.000000\n",
       "Mrs             0.792000\n",
       "Miss            0.697802\n",
       "Master          0.575000\n",
       "Major           0.500000\n",
       "Col             0.500000\n",
       "Dr              0.428571\n",
       "Mr              0.156673\n",
       "Rev             0.000000\n",
       "Jonkheer        0.000000\n",
       "Don             0.000000\n",
       "Capt            0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby('Title').mean()['Survived']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:671: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "df['Is_Married'] = 0\n",
    "df['Is_Married'].loc[df['Title'] == 'Mrs'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      0\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    0\n",
       "888    0\n",
       "889    0\n",
       "890    0\n",
       "Name: Is_Married, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Is_Married']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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