{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7491a04-ef6b-4e2e-93c1-b6e2f58cb730",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ea5ec81-1283-4aec-845e-5f7e1ff76b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f09e355d-dd7f-4b26-bd50-bd5e9b718089",
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
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       "      <td>Futrelle\\t Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>8.0500</td>\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\\t Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings\\t Mrs. John Bradley (Florence Briggs T...  female  38.0      1   \n",
       "2                             Heikkinen\\t Miss Laina  female  26.0      0   \n",
       "3      Futrelle\\t Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                          Allen\\t 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": "21b8eb61-881f-40d7-a5d0-215faec6f65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[['PassengerId','Survived','Pclass','Sex','Age','Ticket','Fare',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5be595e-fefb-4bcd-8244-43ad1643d236",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Ticket</th>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.9250</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
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       "      <td>35.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age            Ticket     Fare\n",
       "0            1         0       3    male  22.0         A/5 21171   7.2500\n",
       "1            2         1       1  female  38.0          PC 17599  71.2833\n",
       "2            3         1       3  female  26.0  STON/O2. 3101282   7.9250\n",
       "3            4         1       1  female  35.0            113803  53.1000\n",
       "4            5         0       3    male  35.0            373450   8.0500"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f26eec6-26ff-4dee-910e-7e34977d7cdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Survived']\n",
    "x=df['Pclass']\n",
    "x_train,x_test,y_train, y_test = train_test_split(x,y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82046214-c1af-4b05-9d09-0728044f81a0",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "incomplete input (1360871968.py, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[17], line 5\u001b[0;36m\u001b[0m\n\u001b[0;31m    \u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
     ]
    }
   ],
   "source": [
    "rf_model =randomforestclassifier()\n",
    "rf_model.fit(x_train,y_train)\n",
    "y_pred = rf_model.predict(x_test)\n",
    "print(classfication_report(y_test,y_pred)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aba91e10-99b0-46de-a777-fd2642608996",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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