day45-feature-construction-and-feature-splitting/day45.ipynb

{
 "cells": [
  {
   "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",
   "execution_count": 87,
   "metadata": {},
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       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</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",
       "3  35.0       1      1      0         1\n",
       "4  35.0       3      0      0         0"
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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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   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
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       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</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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       "4  35.0       3      0      0         0"
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     "execution_count": 89,
     "metadata": {},
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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": {},
   "outputs": [
    {
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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\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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     "execution_count": 91,
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    "X.head()"
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  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6933333333333332"
      ]
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     "execution_count": 92,
     "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": [
    "## 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,
   "metadata": {},
   "outputs": [
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       "      <th>Pclass</th>\n",
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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"
      ]
     },
     "execution_count": 94,
     "metadata": {},
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    "X.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
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     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
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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": {},
   "outputs": [
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       "      <th>Parch</th>\n",
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      "text/plain": [
       "    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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    }
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   "source": [
    "X.head()"
   ]
  },
  {
   "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": {},
   "outputs": [
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      "text/plain": [
       "    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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   "source": [
    "X.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0.7003174603174602"
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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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