titanic_project.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>34.50000</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>47.00000</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>62.00000</td>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0            892         0       3   \n",
       "1            893         1       3   \n",
       "2            894         0       2   \n",
       "3            895         0       3   \n",
       "4            896         1       3   \n",
       "..           ...       ...     ...   \n",
       "413         1305         0       3   \n",
       "414         1306         1       1   \n",
       "415         1307         0       3   \n",
       "416         1308         0       3   \n",
       "417         1309         0       3   \n",
       "\n",
       "                                             Name       Age  SibSp  Parch  \\\n",
       "0                                Kelly, Mr. James  34.50000      0      0   \n",
       "1                Wilkes, Mrs. James (Ellen Needs)  47.00000      1      0   \n",
       "2                       Myles, Mr. Thomas Francis  62.00000      0      0   \n",
       "3                                Wirz, Mr. Albert  27.00000      0      0   \n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)  22.00000      1      1   \n",
       "..                                            ...       ...    ...    ...   \n",
       "413                            Spector, Mr. Woolf  30.27259      0      0   \n",
       "414                  Oliva y Ocana, Dona. Fermina  39.00000      0      0   \n",
       "415                  Saether, Mr. Simon Sivertsen  38.50000      0      0   \n",
       "416                           Ware, Mr. Frederick  30.27259      0      0   \n",
       "417                      Peter, Master. Michael J  30.27259      1      1   \n",
       "\n",
       "                 Ticket      Fare Cabin  Sex_female  Sex_male  Embarked_C  \\\n",
       "0                330911    7.8292   NaN           0         1           0   \n",
       "1                363272    7.0000   NaN           1         0           0   \n",
       "2                240276    9.6875   NaN           0         1           0   \n",
       "3                315154    8.6625   NaN           0         1           0   \n",
       "4               3101298   12.2875   NaN           1         0           0   \n",
       "..                  ...       ...   ...         ...       ...         ...   \n",
       "413           A.5. 3236    8.0500   NaN           0         1           0   \n",
       "414            PC 17758  108.9000  C105           1         0           1   \n",
       "415  SOTON/O.Q. 3101262    7.2500   NaN           0         1           0   \n",
       "416              359309    8.0500   NaN           0         1           0   \n",
       "417                2668   22.3583   NaN           0         1           1   \n",
       "\n",
       "     Embarked_Q  Embarked_S  \n",
       "0             1           0  \n",
       "1             0           1  \n",
       "2             1           0  \n",
       "3             0           1  \n",
       "4             0           1  \n",
       "..          ...         ...  \n",
       "413           0           1  \n",
       "414           0           0  \n",
       "415           0           1  \n",
       "416           0           1  \n",
       "417           0           0  \n",
       "\n",
       "[418 rows x 15 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data = pd.read_csv(\"titanic.csv\")\n",
    "titanic_data.describe()\n",
    "titanic_data = pd.get_dummies(titanic_data, columns=[\"Sex\"])\n",
    "titanic_data = pd.get_dummies(titanic_data,columns=[\"Embarked\"])\n",
    "mean_age = titanic_data['Age'].mean()\n",
    "titanic_data[\"Age\"].fillna(value=mean_age,inplace=True)\n",
    "mean_fare = titanic_data['Fare'].mean()\n",
    "titanic_data[\"Fare\"].fillna(value=mean_fare,inplace=True)\n",
    "titanic_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data[\"Fare\"].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.92000000e+02, 3.00000000e+00, 3.45000000e+01, ...,\n",
       "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
       "       [8.93000000e+02, 3.00000000e+00, 4.70000000e+01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [8.94000000e+02, 2.00000000e+00, 6.20000000e+01, ...,\n",
       "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [1.30700000e+03, 3.00000000e+00, 3.85000000e+01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [1.30800000e+03, 3.00000000e+00, 3.02725904e+01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [1.30900000e+03, 3.00000000e+00, 3.02725904e+01, ...,\n",
       "        1.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = titanic_data.iloc[:,[0,2,4,5,6,8,10,11,12,13,14]].values\n",
    "y = titanic_data.iloc[:,1].values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.17300000e+03, 3.00000000e+00, 7.50000000e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [9.44000000e+02, 2.00000000e+00, 2.00000000e+01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [1.19500000e+03, 3.00000000e+00, 2.40000000e+01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       ...,\n",
       "       [9.07000000e+02, 2.00000000e+00, 2.40000000e+01, ...,\n",
       "        1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.01700000e+03, 3.00000000e+00, 1.70000000e+01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [1.15700000e+03, 3.00000000e+00, 3.02725904e+01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import  train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=10,test_size=.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=10, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "regressor = RandomForestClassifier(random_state=0, n_estimators=10)\n",
    "regressor.fit(x_train, y_train) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [0 0]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = regressor.predict(x_test)\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1),y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[45  0]\n",
      " [ 0 39]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  }
 ],
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