RandomForests (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(668, 11)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</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>Embarked</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>228414</td>\n",
       "      <td>26.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 2466</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250655</td>\n",
       "      <td>26.000</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Mrs. Nils (Alma Cornelia Berglund)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>349909</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
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      "text/plain": [
       "   Pclass                                            Name     Sex   Age  \\\n",
       "0       2   Weisz, Mrs. Leopold (Mathilde Francoise Pede)  female  29.0   \n",
       "1       3               Williams, Mr. Howard Hugh \"Harry\"    male   NaN   \n",
       "2       2  Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")    male  39.0   \n",
       "3       3     Palsson, Mrs. Nils (Alma Cornelia Berglund)  female  29.0   \n",
       "4       3                          Sutehall, Mr. Henry Jr    male  25.0   \n",
       "\n",
       "   SibSp  Parch           Ticket    Fare Cabin Embarked  Survived  \n",
       "0      1      0           228414  26.000   NaN        S         1  \n",
       "1      0      0         A/5 2466   8.050   NaN        S         0  \n",
       "2      0      0           250655  26.000   NaN        S         0  \n",
       "3      0      4           349909  21.075   NaN        S         0  \n",
       "4      0      0  SOTON/OQ 392076   7.050   NaN        S         0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv(\"/Users/raunitsingh/Downloads/0000000000002429_training_titanic_x_y_train.csv\")\n",
    "print(titanic.shape)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>2</td>\n",
       "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>228414</td>\n",
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       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 2466</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250655</td>\n",
       "      <td>26.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Mrs. Nils (Alma Cornelia Berglund)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pclass                                            Name     Sex   Age  \\\n",
       "0       2   Weisz, Mrs. Leopold (Mathilde Francoise Pede)  female  29.0   \n",
       "1       3               Williams, Mr. Howard Hugh \"Harry\"    male   NaN   \n",
       "2       2  Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")    male  39.0   \n",
       "3       3     Palsson, Mrs. Nils (Alma Cornelia Berglund)  female  29.0   \n",
       "4       3                          Sutehall, Mr. Henry Jr    male  25.0   \n",
       "\n",
       "   SibSp  Parch           Ticket    Fare Cabin Embarked  Survived  \n",
       "0      1      0           228414  26.000   NaN        S         1  \n",
       "1      0      0         A/5 2466   8.050   NaN        S         0  \n",
       "2      0      0           250655  26.000   NaN        S         0  \n",
       "3      0      4           349909  21.075   NaN        S         0  \n",
       "4      0      0  SOTON/OQ 392076   7.050   NaN        S         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = titanic.iloc[:, 0 : titanic.shape[1] - 1]\n",
    "Y = titanic.iloc[:, -1]\n",
    "\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</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>Embarked</th>\n",
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       "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
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       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")</td>\n",
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       "      <td>3</td>\n",
       "      <td>Palsson, Mrs. Nils (Alma Cornelia Berglund)</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Pclass                                            Name  Sex   Age  SibSp  \\\n",
       "0       2   Weisz, Mrs. Leopold (Mathilde Francoise Pede)    1  29.0      1   \n",
       "1       3               Williams, Mr. Howard Hugh \"Harry\"    0   NaN      0   \n",
       "2       2  Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")    0  39.0      0   \n",
       "3       3     Palsson, Mrs. Nils (Alma Cornelia Berglund)    1  29.0      0   \n",
       "4       3                          Sutehall, Mr. Henry Jr    0  25.0      0   \n",
       "\n",
       "   Parch           Ticket    Fare Cabin Embarked  \n",
       "0      0           228414  26.000   NaN        S  \n",
       "1      0         A/5 2466   8.050   NaN        S  \n",
       "2      0           250655  26.000   NaN        S  \n",
       "3      4           349909  21.075   NaN        S  \n",
       "4      0  SOTON/OQ 392076   7.050   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fun(s):\n",
    "    if(s == \"male\"):\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "X[\"Sex\"] = X[\"Sex\"].apply(fun)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass        0\n",
      "Name          0\n",
      "Sex           0\n",
      "Age         132\n",
      "SibSp         0\n",
      "Parch         0\n",
      "Ticket        0\n",
      "Fare          0\n",
      "Cabin       514\n",
      "Embarked      1\n",
      "dtype: int64\n",
      "Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Fare        0\n",
      "Embarked    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X.isnull().sum())\n",
    "X = X.fillna(X.median())\n",
    "X.head()\n",
    "del X[\"Cabin\"]\n",
    "del X[\"Name\"]\n",
    "del X[\"Ticket\"]\n",
    "print(X.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1\n",
      "1      1\n",
      "2      1\n",
      "3      1\n",
      "4      1\n",
      "5      0\n",
      "6      1\n",
      "7      1\n",
      "8      1\n",
      "9      1\n",
      "10     1\n",
      "11     0\n",
      "12     1\n",
      "13     2\n",
      "14     2\n",
      "15     1\n",
      "16     1\n",
      "17     1\n",
      "18     1\n",
      "19     2\n",
      "20     1\n",
      "21     0\n",
      "22     1\n",
      "23     1\n",
      "24     0\n",
      "25     1\n",
      "26     1\n",
      "27     0\n",
      "28     1\n",
      "29     1\n",
      "      ..\n",
      "638    1\n",
      "639    0\n",
      "640    1\n",
      "641    1\n",
      "642    1\n",
      "643    1\n",
      "644    1\n",
      "645    1\n",
      "646    1\n",
      "647    0\n",
      "648    1\n",
      "649    1\n",
      "650    1\n",
      "651    1\n",
      "652    1\n",
      "653    1\n",
      "654    1\n",
      "655    1\n",
      "656    1\n",
      "657    1\n",
      "658    1\n",
      "659    2\n",
      "660    1\n",
      "661    0\n",
      "662    1\n",
      "663    1\n",
      "664    2\n",
      "665    1\n",
      "666    1\n",
      "667    2\n",
      "Name: Embarked, Length: 668, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def embarked(s):\n",
    "    if(s == \"C\"):\n",
    "        return 0\n",
    "    elif(s == \"S\"):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "X[\"Embarked\"] = X[\"Embarked\"].apply(embarked)\n",
    "print(X[\"Embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9840319361277445, 0.7724550898203593)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_pred_1 = clf.predict(X_train) \n",
    "Y_pred_2 = clf.predict(X_test)\n",
    "clf.score(X_train, Y_train), clf.score(X_test, Y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8902195608782435, 0.8083832335329342)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf_Random = RandomForestClassifier(max_depth = 6, random_state = 1)\n",
    "clf_Random.fit(X_train, Y_train)\n",
    "Y_pred_1 = clf_Random.predict(X_train)\n",
    "Y_pred_2 = clf_Random.predict(X_test)\n",
    "clf_Random.score(X_train, Y_train), clf_Random.score(X_test, Y_test) "
   ]
  }
 ],
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