getting-started-with-kaggle-titanic-problem.ipynb

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     "text": [
      "/kaggle/input/titanic/gender_submission.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/train.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 5GB 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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       "   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",
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       "                                                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",
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       "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",
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   "source": [
    "train_data = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
    "train_data.head()"
   ]
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
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     "execution_count": 3,
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   ],
   "source": [
    "test_data = pd.read_csv(\"/kaggle/input/titanic/test.csv\")\n",
    "test_data.head()"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of women who survived: 0.7420382165605095\n"
     ]
    }
   ],
   "source": [
    "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
    "rate_women = sum(women)/len(women)\n",
    "\n",
    "print(\"% of women who survived:\", rate_women)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "execution": {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "% of men who survived: 0.18890814558058924\n"
     ]
    }
   ],
   "source": [
    "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
    "rate_men = sum(men)/len(men)\n",
    "\n",
    "print(\"% of men who survived:\", rate_men)"
   ]
  },
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully saved!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "y = train_data[\"Survived\"]\n",
    "features = [\"Pclass\",\"Sex\",\"SibSp\",\"Parch\"]\n",
    "X = pd.get_dummies(train_data[features])\n",
    "X_test = pd.get_dummies(test_data[features])\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth=5,random_state=1)\n",
    "model.fit(X,y)\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "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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