housing-price-prediction-dt/housing-price-prediction-dt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Regression - Housing Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "## -- ML Related LIbraries####\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### File Path = https://drive.google.com/file/d/1hBLdvnZxity2T4OL1NYo0c84LMv8T9rt/view\n",
    "#### FileId = 1hBLdvnZxity2T4OL1NYo0c84LMv8T9rt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset for Housing Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## For Google Drive\n",
    "## file_id = \"1hBLdvnZxity2T4OL1NYo0c84LMv8T9rt\"\n",
    "## url = f\"https://drive.google.com/uc?id={file_id}\"\n",
    "url = 'https://github.com/vmahawar/data-science-datasets-collection/raw/main/housing_prices.csv'\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"mainroad\"]=df[\"mainroad\"].map({\"yes\":1,\"no\":0}) \n",
    "df[\"guestroom\"]=df[\"guestroom\"].map({\"yes\":1,\"no\":0}) \n",
    "df[\"basement\"]=df[\"basement\"].map({\"yes\":1,\"no\":0}) \n",
    "df[\"hotwaterheating\"]=df[\"hotwaterheating\"].map({\"yes\":1,\"no\":0}) \n",
    "df[\"airconditioning\"]=df[\"airconditioning\"].map({\"yes\":1,\"no\":0}) \n",
    "df[\"prefarea\"]=df[\"prefarea\"].map({\"yes\":1,\"no\":0}) \n",
    "df[\"furnishingstatus\"]=df[\"furnishingstatus\"].map({\"furnished\":0,\"semi-furnished\":1,\"unfurnished\":2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Missing Value imputation is not mandatory in Tree Models\n",
    "# Scaling is not mandatory in Tree Models #In industry people do not do it\n",
    "# Outlier Treatment is not mandatory in Tree Models #In industry people do not do it\n",
    "# EDA still has to be done irresepctive of model algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"price\"],axis=1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate the shape of train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try different criterion - MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
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       "\n",
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       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
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       "  padding: 0;\n",
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       "#sk-container-id-1 input.sk-hidden--visually {\n",
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       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
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       "  border: 1px dashed var(--sklearn-color-line);\n",
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       "#sk-container-id-1 div.sk-container {\n",
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "#sk-container-id-1 div.sk-parallel-item {\n",
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       "  align-self: flex-end;\n",
       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
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       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
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       "\n",
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       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
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       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
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       "#sk-container-id-1 div.sk-toggleable__content {\n",
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       "  max-width: 0;\n",
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       "\n",
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       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
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       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(max_depth=2)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeRegressor(max_depth=2,criterion='squared_error')\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8)) \n",
    "plot_tree(dt\n",
    "          ,feature_names=['area', 'bedrooms', 'bathrooms', 'stories', 'mainroad', 'guestroom', 'basement', 'hotwaterheating', 'airconditioning', 'parking', 'prefarea', 'furnishingstatus']) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = dt.predict(X_train)\n",
    "y_test_pred = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default splitting criterion: squared_error\n"
     ]
    }
   ],
   "source": [
    "# Print default parameters\n",
    "print(\"Default splitting criterion:\", dt.criterion)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".ds_venv",
   "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}