Data_Prep_Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "142b2947-001c-4152-93d8-9f4a74c781f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We want to make a model that predicts how likely passengers were to survive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1128f429-8a20-4d32-bca6-bf36c3d0f14f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84bd22b4-bc2a-48bb-b5e1-e4f62a3b150f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ffe4ae3-6df8-4382-82e0-75291bf7915f",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <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",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
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       "  </tbody>\n",
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      ],
      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44c820d3-3718-4448-bf26-0cf83965d76b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "#Mean age is 29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b36f73a-d5c2-455a-96cc-4ad41daf1ada",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fea09a01-4dbd-48ea-b858-1047d4eccfd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#paring Sibling with spouse and parent with child together\n",
    "df['Travelalone'] = np.where((df['SibSp'] + df['Parch']) > 0,0,1).astype('uint8')\n",
    "#we are creating a new variable we call \"travel alone\",\n",
    "#We specifiy that if the sum of sibling spoyse and 'Parch' is greater than 0 then they are not alone,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ed329ae-1966-4c09-ba6e-d2cd2fdc47c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping now uncessesary data columns, not relevant for survivalbility as well as no longer needed\n",
    "# due to new travelalone variable we made\n",
    "\n",
    "df1 = df.drop(['PassengerId', 'Name', 'SibSp', 'Parch', 'Ticket', 'Cabin'], axis =1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35b90873-10c8-44f2-b55e-ee268ef88be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived         0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age            177\n",
       "Fare             0\n",
       "Embarked         2\n",
       "Travelalone      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "332c2e2a-9899-4001-b21c-c7c718858fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing missing values with mean\n",
    "df['Age'].fillna(df1['Age'].median(skipna=True), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9f6e8446-7dda-4da1-ba7c-ae2393ea645c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <td>38.0</td>\n",
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       "      <td>C</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>S</td>\n",
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      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare Embarked  Travelalone\n",
       "0         0       3    male  22.0   7.2500        S            0\n",
       "1         1       1  female  38.0  71.2833        C            0\n",
       "2         1       3  female  26.0   7.9250        S            1\n",
       "3         1       1  female  35.0  53.1000        S            0\n",
       "4         0       3    male  35.0   8.0500        S            1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "db82ad6a-0b6c-40d7-8dfd-fd16edce1f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We see catergorical data for sex, embarked and Pclass\n",
    "#We will encode them with get_dummies\n",
    "\n",
    "df_titanic = pd.get_dummies(df1,columns=['Pclass', 'Embarked', 'Sex'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b3a17cb-9fe3-482b-a72f-cf725801c9e3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.9250</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Travelalone  Pclass_2  Pclass_3  Embarked_Q  \\\n",
       "0         0  22.0   7.2500            0         0         1           0   \n",
       "1         1  38.0  71.2833            0         0         0           0   \n",
       "2         1  26.0   7.9250            1         0         1           0   \n",
       "3         1  35.0  53.1000            0         0         0           0   \n",
       "4         0  35.0   8.0500            1         0         1           0   \n",
       "\n",
       "   Embarked_S  Sex_male  \n",
       "0           1         1  \n",
       "1           0         0  \n",
       "2           1         0  \n",
       "3           1         0  \n",
       "4           1         1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.head()\n",
    "#We can see below now all categorical variables here are dummy values (0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3e1a329b-27e1-4424-afbb-7396eed163c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preprocessing the data\n",
    "\n",
    "#scalar to standardize our data points\n",
    "\n",
    "X = df_titanic.drop(['Survived'], axis = 1) #input data is everything except our output\n",
    "y = df_titanic['Survived'] #output data is ONLY our output\n",
    "\n",
    "#importing sklearn preprocessing library\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77c58d86-cfdc-4853-b2ea-68b001b6ff8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transposing\n",
    "trans_MM = MinMaxScaler()\n",
    "trans_SS = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fc98aa9a-cd1c-48dc-833f-588b76f6bce6",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "            0         1    2    3    4    5    6    7\n",
       "0    0.271174  0.014151  0.0  0.0  1.0  0.0  1.0  1.0\n",
       "1    0.472229  0.139136  0.0  0.0  0.0  0.0  0.0  0.0\n",
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       "4    0.434531  0.015713  1.0  0.0  1.0  0.0  1.0  1.0\n",
       "..        ...       ...  ...  ...  ...  ...  ...  ...\n",
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       "887  0.233476  0.058556  1.0  0.0  0.0  0.0  1.0  0.0\n",
       "888       NaN  0.045771  0.0  0.0  1.0  0.0  1.0  0.0\n",
       "889  0.321438  0.058556  1.0  0.0  0.0  0.0  0.0  1.0\n",
       "890  0.396833  0.015127  1.0  0.0  1.0  1.0  0.0  1.0\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transposing the data\n",
    "df_MM = trans_MM.fit_transform(X)\n",
    "#print data\n",
    "pd.DataFrame(df_MM)\n",
    "#We have transformed the data between the range of 0 and 1\n",
    "#MinMax scaler uses the inputs directly and computes the scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff8c43da-f0a9-485b-86ae-4c67bf0c797d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.510152</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
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       "..        ...       ...       ...       ...       ...       ...       ...   \n",
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       "887 -0.737041 -0.044381  0.811922 -0.510152 -1.107926 -0.307562  0.619306   \n",
       "888       NaN -0.176263 -1.231645 -0.510152  0.902587 -0.307562  0.619306   \n",
       "889 -0.254825 -0.044381  0.811922 -0.510152 -1.107926 -0.307562 -1.614710   \n",
       "890  0.158503 -0.492378  0.811922 -0.510152  0.902587  3.251373 -1.614710   \n",
       "\n",
       "            7  \n",
       "0    0.737695  \n",
       "1   -1.355574  \n",
       "2   -1.355574  \n",
       "3   -1.355574  \n",
       "4    0.737695  \n",
       "..        ...  \n",
       "886  0.737695  \n",
       "887 -1.355574  \n",
       "888 -1.355574  \n",
       "889  0.737695  \n",
       "890  0.737695  \n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now checking our standard scaler method\n",
    "#standard scaler takes the origin - the mean of distrivution and divdied by SD\n",
    "df_SS = trans_SS.fit_transform(X)\n",
    "pd.DataFrame(df_SS)\n",
    "#We see data has negative values as well\n",
    "#It has also converted the dummies we have created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9bc98aa-123d-4058-8ea6-fb03c4e49ea0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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