Decision Trees and RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "ds = pd.read_csv('/Users/adityakumar/Desktop/titanic.csv')\n",
    "ds.info()\n",
    "#sns.heatmap(ds.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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, 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",
       "\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare\n",
       "0         0       3    male  22.0      1      0   7.2500\n",
       "1         1       1  female  38.0      1      0  71.2833\n",
       "2         1       3  female  26.0      0      0   7.9250\n",
       "3         1       1  female  35.0      1      0  53.1000\n",
       "4         0       3    male  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_to_drop=['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked']\n",
    "\n",
    "df=ds.drop(col_to_drop, axis=1)\n",
    "df.head()\n",
    "#sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare\n",
       "0         0       3    0  22.0      1      0   7.2500\n",
       "1         1       1    1  38.0      1      0  71.2833\n",
       "2         1       3    1  26.0      0      0   7.9250\n",
       "3         1       1    1  35.0      1      0  53.1000\n",
       "4         0       3    0  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_sex_to_num(s):\n",
    "    if s=='male':\n",
    "        return 0\n",
    "    elif s=='female':\n",
    "        return 1\n",
    "    else:\n",
    "        return s\n",
    "df.Sex=df.Sex.map(convert_sex_to_num)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.406162</td>\n",
       "      <td>2.236695</td>\n",
       "      <td>0.365546</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.512605</td>\n",
       "      <td>0.431373</td>\n",
       "      <td>34.694514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.491460</td>\n",
       "      <td>0.838250</td>\n",
       "      <td>0.481921</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.929783</td>\n",
       "      <td>0.853289</td>\n",
       "      <td>52.918930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.741700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Sex         Age       SibSp       Parch  \\\n",
       "count  714.000000  714.000000  714.000000  714.000000  714.000000  714.000000   \n",
       "mean     0.406162    2.236695    0.365546   29.699118    0.512605    0.431373   \n",
       "std      0.491460    0.838250    0.481921   14.526497    0.929783    0.853289   \n",
       "min      0.000000    1.000000    0.000000    0.420000    0.000000    0.000000   \n",
       "25%      0.000000    1.000000    0.000000   20.125000    0.000000    0.000000   \n",
       "50%      0.000000    2.000000    0.000000   28.000000    0.000000    0.000000   \n",
       "75%      1.000000    3.000000    1.000000   38.000000    1.000000    1.000000   \n",
       "max      1.000000    3.000000    1.000000   80.000000    5.000000    6.000000   \n",
       "\n",
       "             Fare  \n",
       "count  714.000000  \n",
       "mean    34.694514  \n",
       "std     52.918930  \n",
       "min      0.000000  \n",
       "25%      8.050000  \n",
       "50%     15.741700  \n",
       "75%     33.375000  \n",
       "max    512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df.dropna()\n",
    "data.describe()\n",
    "#data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107334a90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e79c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0, figsize=(10, 10))\n",
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(714, 6) (714, 1)\n"
     ]
    }
   ],
   "source": [
    "x_col=['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']\n",
    "y_col=['Survived']\n",
    "x=data[x_col]\n",
    "y=data[y_col]\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare\n",
       "0         0       3    0  22.0      1      0   7.2500\n",
       "1         1       1    1  38.0      1      0  71.2833\n",
       "2         1       3    1  26.0      0      0   7.9250\n",
       "3         1       1    1  35.0      1      0  53.1000\n",
       "4         0       3    0  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.reset_index(drop=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived     1.000\n",
       "Pclass       3.000\n",
       "Sex          1.000\n",
       "Age         26.000\n",
       "SibSp        0.000\n",
       "Parch        0.000\n",
       "Fare         7.925\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Trees\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def divide_data(xdata, fkey, fval):\n",
    "    xright=pd.DataFrame([], columns=xdata.columns)\n",
    "    xleft=pd.DataFrame([], columns=xdata.columns)\n",
    "    for ix in range(xdata.shape[0]):\n",
    "        val=xdata[fkey].loc[ix]\n",
    "        if val>fval:\n",
    "            xright=xright.append(xdata.loc[ix])#what is the difference between this line and just \n",
    "                                          #xright.append(xdata.loc[ix]) because on using without equal to\n",
    "        else:\n",
    "            xleft=xleft.append(xdata.loc[ix])#the output of next cell coming -1000 for all\n",
    "    return xleft, xright\n",
    "\n",
    "\n",
    "def entropy(col):\n",
    "    counts = np.unique(col, return_counts=True)\n",
    "    N=float(col.shape[0])\n",
    "    ent=0\n",
    "    for ix in range(counts[1].shape[0]):\n",
    "        p=counts[1][ix]/N\n",
    "        ent+=(-1*p*np.log2(p))\n",
    "    return ent\n",
    "\n",
    "\n",
    "def information_gain(xdata, fkey, fval):\n",
    "    left, right=divide_data(xdata, fkey, fval)\n",
    "    P=float(left.shape[0])/xdata.shape[0]\n",
    "    Q=float(right.shape[0])/xdata.shape[0]\n",
    "    if left.shape[0]==0 or right.shape[0]==0:\n",
    "        return -1000\n",
    "    \n",
    "    \n",
    "    return entropy(xdata.Survived)-(P*entropy(left.Survived)+Q*entropy(right.Survived))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass 0.0841581440715109\n",
      "Sex 0.21601606075154256\n",
      "Age 2.6666107433293007e-08\n",
      "SibSp 0.006904127996153919\n",
      "Parch 0.019278172321014586\n",
      "Fare 0.05371958996365245\n"
     ]
    }
   ],
   "source": [
    "for fx in x.columns:\n",
    "    print(fx, information_gain(data, fx, data[fx].mean())) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decision_Tree:\n",
    "    def __init__(self, depth=0, maxdepth=5):\n",
    "        self.right=None\n",
    "        self.left=None\n",
    "        self.depth=depth\n",
    "        self.maxdepth=maxdepth\n",
    "        self.fkey=None\n",
    "        self.fval=None\n",
    "        self.target=None\n",
    "        \n",
    "        \n",
    "    def train(self, xtrain):\n",
    "        print(self.depth,'-'*20)\n",
    "        features=['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']\n",
    "        gain=[]\n",
    "        for fx in features:\n",
    "            gain.append(information_gain(xtrain, fx, xtrain[fx].mean()))\n",
    "        \n",
    "        \n",
    "        self.fkey=features[np.argmax(gain)]\n",
    "        self.fval=xtrain[self.fkey].mean()\n",
    "        \n",
    "        \n",
    "        data_left, data_right=divide_data(xtrain, self.fkey, self.fval)\n",
    "        data_left=data_left.reset_index(drop=True)\n",
    "        data_right=data_right.reset_index(drop=True)\n",
    "        \n",
    "        \n",
    "        if data_left.shape[0]==0 or data_right.shape[0]==0:\n",
    "            if xtrain.Survived.mean()>0.5:\n",
    "                self.target=\"Survived\"\n",
    "            else:\n",
    "                self.target=\"Dead\"\n",
    "                \n",
    "            return\n",
    "        \n",
    "        if self.depth>=self.maxdepth:\n",
    "            if xtrain.Survived.mean()>0.5:\n",
    "                self.target='Survived'\n",
    "            else:\n",
    "                self.target='Dead'\n",
    "            return\n",
    "        \n",
    "        \n",
    "        self.right=Decision_Tree(depth=self.depth+1, maxdepth=self.maxdepth)\n",
    "        self.right.train(data_right)\n",
    "        \n",
    "        self.left=Decision_Tree(depth=self.depth+1, maxdepth=self.maxdepth)\n",
    "        self.left.train(data_left)\n",
    "        \n",
    "        if xtrain.Survived.mean() >= 0.5:\n",
    "            self.target = 'Survived'\n",
    "        else:\n",
    "            self.target = 'Dead'\n",
    "        \n",
    "        return\n",
    "    \n",
    "    \n",
    "     \n",
    "    def predict(self, test):\n",
    "        if test[self.fkey] > self.fval:\n",
    "            # go right\n",
    "            if self.right is None:\n",
    "                return self.target\n",
    "            return self.right.predict(test)\n",
    "        else:\n",
    "            # go left\n",
    "            if self.left is None:\n",
    "                return self.target\n",
    "            return self.left.predict(test)\n",
    "        \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = int(0.8 * data.shape[0])\n",
    "\n",
    "training_data = data[:split]\n",
    "testing_data = data[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 --------------------\n",
      "1 --------------------\n",
      "2 --------------------\n",
      "3 --------------------\n",
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      "3 --------------------\n",
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      "3 --------------------\n",
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      "2 --------------------\n",
      "3 --------------------\n",
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      "3 --------------------\n",
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      "5 --------------------\n",
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      "2 --------------------\n",
      "3 --------------------\n",
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      "3 --------------------\n",
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      "5 --------------------\n",
      "5 --------------------\n",
      "4 --------------------\n",
      "5 --------------------\n",
      "5 --------------------\n"
     ]
    }
   ],
   "source": [
    "dt = Decision_Tree()\n",
    "dt.train(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex 0.36777583187390545\n",
      "Pclass 2.080952380952381\n"
     ]
    }
   ],
   "source": [
    "print(dt.fkey, dt.fval)\n",
    "print(dt.right.fkey, dt.right.fval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dead\n",
      "Survived\n",
      "Dead\n",
      "Dead\n",
      "Dead\n",
      "Survived\n",
      "Dead\n",
      "Survived\n",
      "Dead\n",
      "Dead\n"
     ]
    }
   ],
   "source": [
    "for ix in testing_data.index[:10]:\n",
    "    print(dt.predict(testing_data.loc[ix]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Sex   Age  SibSp  Parch     Fare\n",
       "571         0       3    0  33.0      0      0   7.7750\n",
       "572         1       2    1   6.0      0      1  33.0000\n",
       "573         0       3    0  17.0      1      0   7.0542\n",
       "574         0       2    0  34.0      0      0  13.0000\n",
       "575         0       2    0  50.0      0      0  13.0000\n",
       "576         1       1    0  27.0      1      0  53.1000\n",
       "577         0       3    0  20.0      0      0   8.6625\n",
       "578         1       2    1  30.0      3      0  21.0000\n",
       "579         0       2    0  25.0      1      0  26.0000\n",
       "580         0       3    1  25.0      1      0   7.9250"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree and Random Forests using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = DecisionTreeClassifier()\n",
    "ds.fit(x[:split], y[:split])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DecisionTreeClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-8bd89e76a794>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'DecisionTreeClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "ds = DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7832167832167832"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.score(x[split:], y[split:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier(n_estimators=100)\n",
    "rf.fit(x[:split], y[:split])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8671328671328671"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(x[split:], y[split:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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