DECISION TREES TITANIC DATASET.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from io import StringIO\n",
    "from sklearn import model_selection\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "# it creates the graph within the notebook\n",
    "%matplotlib inline \n",
    "import os\n",
    "os.listdir() # returns list of all files and directories in the specified directory"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "# sklearn module only takes numerical inputs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('Titanic_Dataset_train.csv')\n",
    "test = pd.read_csv('Titanic_Dataset_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>668.000000</td>\n",
       "      <td>536.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.296407</td>\n",
       "      <td>29.700560</td>\n",
       "      <td>0.528443</td>\n",
       "      <td>0.407186</td>\n",
       "      <td>32.064552</td>\n",
       "      <td>0.402695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.831638</td>\n",
       "      <td>14.240257</td>\n",
       "      <td>1.080327</td>\n",
       "      <td>0.854695</td>\n",
       "      <td>45.320835</td>\n",
       "      <td>0.490808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.000000</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>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.925000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
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       "           Pclass         Age       SibSp       Parch        Fare    Survived\n",
       "count  668.000000  536.000000  668.000000  668.000000  668.000000  668.000000\n",
       "mean     2.296407   29.700560    0.528443    0.407186   32.064552    0.402695\n",
       "std      0.831638   14.240257    1.080327    0.854695   45.320835    0.490808\n",
       "min      1.000000    0.670000    0.000000    0.000000    0.000000    0.000000\n",
       "25%      2.000000   21.000000    0.000000    0.000000    7.925000    0.000000\n",
       "50%      3.000000   29.000000    0.000000    0.000000   14.750000    0.000000\n",
       "75%      3.000000   38.250000    1.000000    0.000000   31.275000    1.000000\n",
       "max      3.000000   80.000000    8.000000    6.000000  512.329200    1.000000"
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     "execution_count": 100,
     "metadata": {},
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   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
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       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "    <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",
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       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
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       "      <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",
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       "      <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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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "   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  "
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     "execution_count": 101,
     "metadata": {},
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   "source": [
    "train.head()"
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   "cell_type": "code",
   "execution_count": 102,
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       "      <th>Pclass</th>\n",
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       "      <th>count</th>\n",
       "      <td>223.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>223.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.345291</td>\n",
       "      <td>29.694775</td>\n",
       "      <td>0.506726</td>\n",
       "      <td>0.304933</td>\n",
       "      <td>32.622551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.850047</td>\n",
       "      <td>15.398053</td>\n",
       "      <td>1.169700</td>\n",
       "      <td>0.634108</td>\n",
       "      <td>61.062047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</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>2.000000</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.879200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.035400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "           Pclass         Age       SibSp       Parch        Fare\n",
       "count  223.000000  178.000000  223.000000  223.000000  223.000000\n",
       "mean     2.345291   29.694775    0.506726    0.304933   32.622551\n",
       "std      0.850047   15.398053    1.169700    0.634108   61.062047\n",
       "min      1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      2.000000   19.250000    0.000000    0.000000    7.879200\n",
       "50%      3.000000   27.000000    0.000000    0.000000   12.475000\n",
       "75%      3.000000   37.750000    1.000000    0.000000   30.035400\n",
       "max      3.000000   71.000000    8.000000    2.000000  512.329200"
      ]
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     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "test.describe()"
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       "      <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>2</td>\n",
       "      <td>Davies, Master. John Morgan Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 33112</td>\n",
       "      <td>36.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Leader, Dr. Alice (Farnham)</td>\n",
       "      <td>female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17465</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Kilgannon, Mr. Thomas J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36865</td>\n",
       "      <td>7.7375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>243847</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>McGough, Mr. James Robert</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17473</td>\n",
       "      <td>26.2875</td>\n",
       "      <td>E25</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       2                     Davies, Master. John Morgan Jr    male   8.0   \n",
       "1       1                        Leader, Dr. Alice (Farnham)  female  49.0   \n",
       "2       3                            Kilgannon, Mr. Thomas J    male   NaN   \n",
       "3       2  Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...  female  24.0   \n",
       "4       1                          McGough, Mr. James Robert    male  36.0   \n",
       "\n",
       "   SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "0      1      1  C.A. 33112  36.7500   NaN        S  \n",
       "1      0      0       17465  25.9292   D17        S  \n",
       "2      0      0       36865   7.7375   NaN        Q  \n",
       "3      2      1      243847  27.0000   NaN        S  \n",
       "4      0      0    PC 17473  26.2875   E25        S  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here the first column is the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(668, 11)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(223, 10)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Survived      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()\n",
    "#checking NaN values in each column of train dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### difference b/w labels and features : \n",
    "Features are the fields used as input and labels are used as output. As a simple example, consider how to predict whether one should sell a car based on car mileage, year, etc. Yes/no is the label whereas the mileage and year would be the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    484\n",
       "C    133\n",
       "Q     50\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# onehotencoding, lableencoder\n",
    "# Embarked, Sex = labelencoder.\n",
    "## drop Name,Ticket, Cabin\n",
    "## mean = age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LabelEncoder"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In machine learning, we usually deal with datasets that contain multiple labels in one or more than one column. These labels can be in the form of words or numbers. To make the data understandable or in human-readable form, the training data is often labelled in words. \n",
    "\n",
    "Label Encoding refers to converting the labels into a numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 3])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame.astype() method is used to cast a pandas object to a specified dtype\n",
    "train['Embarked'] = train['Embarked'].astype(str)\n",
    "train['Embarked'] = label_encoder.fit_transform(train['Embarked'])\n",
    "train['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Sex'] = label_encoder.fit_transform(train['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanage = train['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanage = int(meanage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train['Age'].fillna(meanage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 2466</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250655</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2</td>\n",
       "      <td>Ilett, Miss. Bertha</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SO/C 14885</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>3</td>\n",
       "      <td>Morrow, Mr. Thomas Rowan</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>372622</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>3</td>\n",
       "      <td>Bing, Mr. Lee</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>3</td>\n",
       "      <td>Strandberg, Miss. Ida Sofia</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7553</td>\n",
       "      <td>9.8375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>3</td>\n",
       "      <td>Murphy, Miss. Katherine \"Kate\"</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>367230</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>668 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass                                            Name  Sex   Age  SibSp  \\\n",
       "0         2   Weisz, Mrs. Leopold (Mathilde Francoise Pede)    0  29.0      1   \n",
       "1         3               Williams, Mr. Howard Hugh \"Harry\"    1  29.0      0   \n",
       "2         2  Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")    1  39.0      0   \n",
       "3         3     Palsson, Mrs. Nils (Alma Cornelia Berglund)    0  29.0      0   \n",
       "4         3                          Sutehall, Mr. Henry Jr    1  25.0      0   \n",
       "..      ...                                             ...  ...   ...    ...   \n",
       "663       2                             Ilett, Miss. Bertha    0  17.0      0   \n",
       "664       3                        Morrow, Mr. Thomas Rowan    1  29.0      0   \n",
       "665       3                                   Bing, Mr. Lee    1  32.0      0   \n",
       "666       3                     Strandberg, Miss. Ida Sofia    0  22.0      0   \n",
       "667       3                  Murphy, Miss. Katherine \"Kate\"    0  29.0      1   \n",
       "\n",
       "     Parch           Ticket     Fare Cabin  Embarked  Survived  \n",
       "0        0           228414  26.0000   NaN         2         1  \n",
       "1        0         A/5 2466   8.0500   NaN         2         0  \n",
       "2        0           250655  26.0000   NaN         2         0  \n",
       "3        4           349909  21.0750   NaN         2         0  \n",
       "4        0  SOTON/OQ 392076   7.0500   NaN         2         0  \n",
       "..     ...              ...      ...   ...       ...       ...  \n",
       "663      0       SO/C 14885  10.5000   NaN         2         1  \n",
       "664      0           372622   7.7500   NaN         1         0  \n",
       "665      0             1601  56.4958   NaN         2         1  \n",
       "666      0             7553   9.8375   NaN         2         0  \n",
       "667      0           367230  15.5000   NaN         1         1  \n",
       "\n",
       "[668 rows x 11 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['Name','Ticket','Cabin'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.0500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8375</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>668 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Survived\n",
       "0         2    0  29.0      1      0  26.0000         2         1\n",
       "1         3    1  29.0      0      0   8.0500         2         0\n",
       "2         2    1  39.0      0      0  26.0000         2         0\n",
       "3         3    0  29.0      0      4  21.0750         2         0\n",
       "4         3    1  25.0      0      0   7.0500         2         0\n",
       "..      ...  ...   ...    ...    ...      ...       ...       ...\n",
       "663       2    0  17.0      0      0  10.5000         2         1\n",
       "664       3    1  29.0      0      0   7.7500         1         0\n",
       "665       3    1  32.0      0      0  56.4958         2         1\n",
       "666       3    0  22.0      0      0   9.8375         2         0\n",
       "667       3    0  29.0      1      0  15.5000         1         1\n",
       "\n",
       "[668 rows x 8 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(668, 8)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "Survived    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum() # it removed the missing values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to build any model using sklearn we have to specifically define the target variable and all the \n",
    "# independent variables \n",
    "# Dependent variables are the output of the process\n",
    "# Independent variables are the input of the process\n",
    "# stratify will make similar distribution of classes in both the training data and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['Survived'] # 'Survived' is the target variable\n",
    "x = train.drop(['Survived'], axis = 1) # we stored all the other variables as independent variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split # using this we create a validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x, y,random_state = 101, stratify=y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python provides the preprocessing library, which contains the normalize function to normalize \n",
    "# the data. It takes an array in as an input and normalizes its values between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.596806\n",
       "1    0.403194\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize = True) # normalize returns the %age\n",
    "#here 0 : 59% are 0 \n",
    "\n",
    "#here 1 : 40% are 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.598802\n",
       "1    0.401198\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((501, 7), (501,))"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((167, 7), (167,))"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is a classification problem where we have to predict whether the passengers on the titanic will \n",
    "#survive or will not survive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor # it is used in case of regression problems  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_model = DecisionTreeClassifier(random_state = 10) # we create the decision tree model \n",
    "# in random_state we can choose any integer value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=10)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.fit(x_train,y_train)\n",
    "#default criteria of spliting is gini and we can change it to chi-square as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9800399201596807"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(x_train,y_train) # training accuracy is very high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7844311377245509"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(x_test,y_test) # but testing accuracy is not that high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.predict(x_test) # we make prediction for all the testing data points "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.5       , 0.5       ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.5       , 0.5       ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.75      , 0.25      ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.66666667, 0.33333333],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.75      , 0.25      ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.75      , 0.25      ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.85714286, 0.14285714],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.85714286, 0.14285714]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.predict_proba(x_test) # 1st column is for class 0 and 2nd column is for class 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can also use different thresholds to decide the class observation \n",
    "# when we use the predict function it takes 0.5 as the default threshold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dt_model.predict_proba(x_test)[:,1] # we take the 1st column predicting the probabilities of\n",
    "                                             # 1 and we save it in a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_y = []\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.6: # the default thershold was 0.5 but we take 0.6 as the threshold\n",
    "        new_y.append(0) #any prob < 0.6 will be taken as 0\n",
    "    else:\n",
    "        new_y.append(1) #any prob > 0.6 will be taken as 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7844311377245509"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,new_y) \n",
    "# it is not necessary that after changing the threshold the accuracy will increase, it might decrease \n",
    "# as well\n",
    "\n",
    "#max-depth : it tells us the longest path of the tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing Max Depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will train the decision tree model 10 times and change the parameter max depth from 1 to 10\n",
    "#for each model we are storing the training accuracy and testing accuracy\n",
    "\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "for depth in range(1,10):\n",
    "    dt_model = DecisionTreeClassifier(max_depth = depth, random_state = 10)\n",
    "    dt_model.fit(x_train,y_train)\n",
    "    train_accuracy.append(dt_model.score(x_train,y_train))\n",
    "    test_accuracy.append(dt_model.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>max_depth</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>test_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.772455</td>\n",
       "      <td>0.784431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.778443</td>\n",
       "      <td>0.772455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.810379</td>\n",
       "      <td>0.802395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.812375</td>\n",
       "      <td>0.778443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.848303</td>\n",
       "      <td>0.814371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  train_acc  test_acc\n",
       "0          1   0.772455  0.784431\n",
       "1          2   0.778443  0.772455\n",
       "2          3   0.810379  0.802395\n",
       "3          4   0.812375  0.778443\n",
       "4          5   0.848303  0.814371"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we will create a dataframe from the values which we got from the previous cell\n",
    "frame = pd.DataFrame({'max_depth':range(1,10), 'train_acc':train_accuracy, 'test_acc':test_accuracy})\n",
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x215112fc370>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "plt.plot(frame['max_depth'], frame['train_acc'], marker = 'o', color = 'magenta', label = 'train_acc')\n",
    "plt.plot(frame['max_depth'], frame['test_acc'], marker = 'o', color = 'limegreen', label = 'test_acc')\n",
    "plt.xlabel('Depth Of Tree', fontsize = 14)\n",
    "plt.ylabel('Performance', fontsize = 14)\n",
    "plt.grid(color = 'navy')\n",
    "plt.legend()\n",
    "\n",
    "# lower value of max_depth did not allowed the model to learn the pattern and we got low performace which is underfitting\n",
    "# as max_depth is increasing both the training and testing accuracy is increasing \n",
    "# 5 is the max_depth where testing accuracy is the highest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. max_leaf_nodes : Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction \n",
    "#                     in impurity. If None then unlimited number of leaf nodes.\n",
    "\n",
    "# 2. min_samples_split: int or float, default=2. The minimum number of samples required to split an internal node\n",
    "\n",
    "# 3. min_samples_leaf: int or float, default=1. The minimum number of samples required to be at a leaf node.     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We found the optimum value for an individual parameter, we can try it with a combination of paramters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "## using combination of max_depth and max_leaf nodes\n",
    "\n",
    "dt_model = DecisionTreeClassifier(max_depth = 8, max_leaf_nodes = 25, random_state = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=8, max_leaf_nodes=25, random_state=10)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8862275449101796"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8023952095808383"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(x_test,y_test) # the testing accuracy improved and both the training and testing \n",
    "                              # accuracy are in sync\n",
    "                              # the model we have created is a more generalized model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid search helps us to find the optimum value for a combination of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "from IPython.display import Image\n",
    "from io import StringIO\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the max_depth here means that the decision tree will be printed till max_depth = 2, but the dt_model \n",
    "# is trained till the depth that we specified while training the model\n",
    "dot_data = StringIO()\n",
    "export_graphviz(dt_model, out_file = dot_data, max_depth = 3, feature_names = x_train.columns, special_characters = True, rounded = True, filled = True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applications of Decision Tree:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Fraud detection\n",
    "* Credit risk\n",
    "* Loan default : Default is the failure to repay a debt, including interest or principal, on a loan                   or security. \n",
    "* Predicting sales of a retail outlet (or any other business!)\n",
    "* Number of bikes rented\n",
    "* Employee attrition : Employee attrition is defined as the natural process by which employees leave                         the workforce\n",
    "* Customer churn : Customer churn is the percentage of customers that stopped using your company's                       product or service during a certain time frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we grow a tree more than we should, we are likely to end up having an overfit model. Overfitting occurs when a tree fits the training set too well. Having very different accuracies on training and test sets is a strong indication of overfitting. In such cases, we should control the tree growth to obtain a well-generalized model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}