3_Feature_Engineering.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Suvivor Analysis\n",
    "\n",
    "## By: Tahsin Jahin Khalid\n",
    "\n",
    "### Part III: Data Preparation for Model Selection"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Essential Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore deprecation/other warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df = pd.read_pickle(\"interim_data/preprocessed.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df.drop(\"PassengerId\", axis=1, inplace=True)\n",
    "titanic_df.drop(\"Ticket\", axis=1, inplace=True)\n",
    "titanic_df.drop(\"Name\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a categorical according to the Kaggle data card\n",
    "titanic_df[\"Pclass\"] = titanic_df[\"Pclass\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>0</th>\n",
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       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0      3    male  22.0      1      0   7.2500        S\n",
       "1         1      1  female  38.0      1      0  71.2833        C\n",
       "2         1      3  female  26.0      0      0   7.9250        S\n",
       "3         1      1  female  35.0      1      0  53.1000        S\n",
       "4         0      3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = pd.get_dummies(titanic_df, prefix=[\"Pclass\", \"Sex\", \"Embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  Pclass_1  Pclass_2  Pclass_3  \\\n",
       "0         0  22.0      1      0   7.2500     False     False      True   \n",
       "1         1  38.0      1      0  71.2833      True     False     False   \n",
       "2         1  26.0      0      0   7.9250     False     False      True   \n",
       "3         1  35.0      1      0  53.1000      True     False     False   \n",
       "4         0  35.0      0      0   8.0500     False     False      True   \n",
       "5         0  28.0      0      0   8.4583     False     False      True   \n",
       "6         0  54.0      0      0  51.8625      True     False     False   \n",
       "7         0   2.0      3      1  21.0750     False     False      True   \n",
       "8         1  27.0      0      2  11.1333     False     False      True   \n",
       "9         1  14.0      1      0  30.0708     False      True     False   \n",
       "\n",
       "   Sex_female  Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0       False      True       False       False        True  \n",
       "1        True     False        True       False       False  \n",
       "2        True     False       False       False        True  \n",
       "3        True     False       False       False        True  \n",
       "4       False      True       False       False        True  \n",
       "5       False      True       False        True       False  \n",
       "6       False      True       False       False        True  \n",
       "7       False      True       False       False        True  \n",
       "8        True     False       False       False        True  \n",
       "9        True     False        True       False       False  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the new columns to int\n",
    "col_lst = list(df_encoded.columns)\n",
    "col_lst = col_lst[-8:]\n",
    "for col in col_lst:\n",
    "    df_encoded[col] = df_encoded[col].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  Pclass_1  Pclass_2  Pclass_3  \\\n",
       "0         0  22.0      1      0   7.2500         0         0         1   \n",
       "1         1  38.0      1      0  71.2833         1         0         0   \n",
       "2         1  26.0      0      0   7.9250         0         0         1   \n",
       "3         1  35.0      1      0  53.1000         1         0         0   \n",
       "4         0  35.0      0      0   8.0500         0         0         1   \n",
       "5         0  28.0      0      0   8.4583         0         0         1   \n",
       "6         0  54.0      0      0  51.8625         1         0         0   \n",
       "7         0   2.0      3      1  21.0750         0         0         1   \n",
       "8         1  27.0      0      2  11.1333         0         0         1   \n",
       "9         1  14.0      1      0  30.0708         0         1         0   \n",
       "\n",
       "   Sex_female  Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0           0         1           0           0           1  \n",
       "1           1         0           1           0           0  \n",
       "2           1         0           0           0           1  \n",
       "3           1         0           0           0           1  \n",
       "4           0         1           0           0           1  \n",
       "5           0         1           0           1           0  \n",
       "6           0         1           0           0           1  \n",
       "7           0         1           0           0           1  \n",
       "8           1         0           0           0           1  \n",
       "9           1         0           1           0           0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary of Notebook\n",
    "\n",
    "We have dummy encoded the three categorical columns and the dataset is now ready to be used in Model selection"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make Pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded.to_pickle(\"interim_data/ready_ML.pkl\")"
   ]
  }
 ],
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