kaggle_titanic.ipynb

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     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
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   "source": [
    "train_data = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "test_data = pd.read_csv('/kaggle/input/titanic/test.csv')"
   ]
  },
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     "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": [
    "train_data.info()"
   ]
  },
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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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
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   "source": [
    "train_data['Title'] = train_data['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())\n",
    "test_data['Title'] = test_data['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())\n",
    "\n",
    "train_data['Title'].replace(['Mme', 'Ms', 'Lady', 'Mlle', 'the Countess', 'Dona'], 'Miss', inplace=True)\n",
    "test_data['Title'].replace(['Mme', 'Ms', 'Lady', 'Mlle', 'the Countess', 'Dona'], 'Miss', inplace=True)\n",
    "train_data['Title'].replace(['Major', 'Col', 'Capt', 'Don', 'Sir', 'Jonkheer'], 'Mr', inplace=True)\n",
    "test_data['Title'].replace(['Major', 'Col', 'Capt', 'Don', 'Sir', 'Jonkheer'], 'Mr', inplace=True)\n",
    "\n",
    "train_data['Ticket_lett'] = train_data.Ticket.apply(lambda x: x[:2])\n",
    "test_data['Ticket_lett'] = test_data.Ticket.apply(lambda x: x[:2])\n",
    "\n",
    "train_data['Ticket_len'] = train_data.Ticket.apply(lambda x: len(x))\n",
    "test_data['Ticket_len'] = test_data.Ticket.apply(lambda x: len(x))\n",
    "\n",
    "train_data['Fam_size'] = train_data['SibSp'] + train_data['Parch'] + 1\n",
    "test_data['Fam_size'] = test_data['SibSp'] + test_data['Parch'] + 1\n",
    "\n",
    "train_data['Fam_type'] = pd.cut(train_data.Fam_size, [0,1,4,7,11], labels=['Solo', 'Small', 'Big', 'Very big'])\n",
    "test_data['Fam_type'] = pd.cut(test_data.Fam_size, [0,1,4,7,11], labels=['Solo', 'Small', 'Big', 'Very big'])"
   ]
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   "source": [
    "y = train_data[\"Survived\"].copy()\n",
    "features = ['Pclass', 'Fare', 'Title', 'Embarked', 'Fam_type', 'Ticket_len', 'Ticket_lett']\n",
    "X = train_data[features].copy()"
   ]
  },
  {
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    {
     "name": "stdout",
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     "text": [
      "Cross validation score: 0.831\n"
     ]
    }
   ],
   "source": [
    "numerical_cols = ['Fare']\n",
    "categorical_cols = ['Pclass', 'Title', 'Embarked', 'Fam_type', 'Ticket_len', 'Ticket_lett']\n",
    "\n",
    "# Preprocessing for numerical data\n",
    "numerical_transformer = SimpleImputer(strategy='median')\n",
    "\n",
    "# Preprocessing for categorical data\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "# Bundle preprocessing for numerical and categorical data\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numerical_transformer, numerical_cols),\n",
    "        ('cat', categorical_transformer, categorical_cols)\n",
    "    ])\n",
    "\n",
    "# Bundle preprocessing and modeling code \n",
    "titanic_pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('model', xgb.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "          colsample_bynode=1, colsample_bytree=1, gamma=1.25, gpu_id=-1,\n",
    "          importance_type='gain', interaction_constraints='',\n",
    "          learning_rate=0.4, max_delta_step=0, max_depth=5,\n",
    "          min_child_weight=1, missing=None, monotone_constraints='()',\n",
    "          n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=42,\n",
    "          reg_alpha=0, reg_lambda=1.0, scale_pos_weight=1, seed=42,\n",
    "          subsample=0.8, tree_method='exact', use_label_encoder=False,\n",
    "          validate_parameters=1, verbosity=None, eval_metric='aucpr'))\n",
    "])\n",
    "\n",
    "# Preprocessing of training data, fit model \n",
    "titanic_pipeline.fit(X, y)\n",
    "\n",
    "print('Cross validation score: {:.3f}'.format(cross_val_score(titanic_pipeline, X, y, cv=10).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T21:23:03.657749Z",
     "iopub.status.busy": "2021-03-07T21:23:03.656951Z",
     "iopub.status.idle": "2021-03-07T21:23:03.667153Z",
     "shell.execute_reply": "2021-03-07T21:23:03.667624Z"
    },
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     "exception": false,
     "start_time": "2021-03-07T21:23:03.637510",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test = test_data[features]\n",
    "predictions = titanic_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T21:23:03.693178Z",
     "iopub.status.busy": "2021-03-07T21:23:03.692607Z",
     "iopub.status.idle": "2021-03-07T21:23:03.701381Z",
     "shell.execute_reply": "2021-03-07T21:23:03.700547Z"
    },
    "papermill": {
     "duration": 0.022764,
     "end_time": "2021-03-07T21:23:03.701552",
     "exception": false,
     "start_time": "2021-03-07T21:23:03.678788",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully saved!\n"
     ]
    }
   ],
   "source": [
    "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n",
    "output.to_csv('my_submission.csv', index=False)\n",
    "print('Your submission was successfully saved!')"
   ]
  }
 ],
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