{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d28218d",
   "metadata": {},
   "source": [
    "# Decision Trees with Gini Impurity: A Complete Guide\n",
    "\n",
    "**Learning Objectives:**\n",
    "- Understand the intuition behind decision trees\n",
    "- Learn how Gini Impurity measures node purity\n",
    "- Implement decision tree algorithms from scratch\n",
    "- Visualize decision boundaries and tree structures\n",
    "- Compare with scikit-learn implementations\n",
    "\n",
    "**Why Decision Trees?**\n",
    "- **Interpretable**: Easy to understand and explain\n",
    "- **No assumptions**: Works with any type of data\n",
    "- **Feature selection**: Automatically identifies important features\n",
    "- **Handles missing data**: Can work with incomplete datasets\n",
    "- **Foundation**: Basis for Random Forests and Gradient Boosting\n",
    "\n",
    "Let's build our understanding step by step!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1451ed91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to Decision Trees with Gini Impurity\n",
      "We will learn how computers make decisions using tree-based algorithms\n",
      "\n",
      "Tennis Playing Decision Dataset:\n",
      "This classic dataset helps decide whether to play tennis based on weather conditions\n",
      "  Temperature Humidity    Wind Play_Tennis\n",
      "0         Hot     High    Weak          No\n",
      "1         Hot     High  Strong          No\n",
      "2         Hot   Normal    Weak         Yes\n",
      "3        Mild     High    Weak         Yes\n",
      "4        Cool   Normal    Weak         Yes\n",
      "5        Cool   Normal  Strong          No\n",
      "6        Cool   Normal  Strong         Yes\n",
      "7        Mild     High    Weak          No\n",
      "8        Cool   Normal    Weak         Yes\n",
      "9        Mild   Normal    Weak         Yes\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import make_classification, load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Set style for better plots\n",
    "plt.style.use('default')\n",
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "plt.rcParams['font.size'] = 10\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "print(\"Welcome to Decision Trees with Gini Impurity\")\n",
    "print(\"We will learn how computers make decisions using tree-based algorithms\")\n",
    "\n",
    "# Create a simple educational dataset\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create a toy dataset for weather prediction\n",
    "weather_data = {\n",
    "    'Temperature': ['Hot', 'Hot', 'Hot', 'Mild', 'Cool', 'Cool', 'Cool', 'Mild', 'Cool', 'Mild', 'Mild', 'Hot', 'Mild', 'Cool'],\n",
    "    'Humidity': ['High', 'High', 'Normal', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'Normal', 'High', 'Normal', 'High', 'High'],\n",
    "    'Wind': ['Weak', 'Strong', 'Weak', 'Weak', 'Weak', 'Strong', 'Strong', 'Weak', 'Weak', 'Weak', 'Strong', 'Strong', 'Weak', 'Strong'],\n",
    "    'Play_Tennis': ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No']\n",
    "}\n",
    "\n",
    "df_weather = pd.DataFrame(weather_data)\n",
    "print(\"\\nTennis Playing Decision Dataset:\")\n",
    "print(\"This classic dataset helps decide whether to play tennis based on weather conditions\")\n",
    "print(df_weather.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a91f89c",
   "metadata": {},
   "source": [
    "## Understanding Gini Impurity\n",
    "\n",
    "**What is Gini Impurity?**\n",
    "Gini Impurity measures how \"impure\" or \"mixed\" a set of examples is. It answers the question: *\"If I randomly pick an example and randomly guess its class, what's the probability I'll be wrong?\"*\n",
    "\n",
    "**Mathematical Formula:**\n",
    "```\n",
    "Gini Impurity = 1 - Σ(pi)²\n",
    "```\n",
    "Where pi is the probability of class i\n",
    "\n",
    "**Intuition:**\n",
    "- **Gini = 0**: Perfect purity (all examples belong to same class)\n",
    "- **Gini = 0.5**: Maximum impurity for binary classification (50-50 split)\n",
    "- **Lower Gini**: Better, more pure node\n",
    "\n",
    "**Example:**\n",
    "- 100 examples: 90 \"Yes\", 10 \"No\"\n",
    "- p(Yes) = 0.9, p(No) = 0.1  \n",
    "- Gini = 1 - (0.9² + 0.1²) = 1 - (0.81 + 0.01) = 0.18\n",
    "\n",
    "Let's implement this step by step!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1bd78262",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GINI IMPURITY EXAMPLES\n",
      "==================================================\n",
      "Example 1 - All 'Yes': ['Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes']\n",
      "Gini Impurity: 0.000 (Perfect purity!)\n",
      "\n",
      "Example 2 - Equal split: ['Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'No', 'No', 'No', 'No']\n",
      "Gini Impurity: 0.500 (Maximum impurity for binary)\n",
      "\n",
      "Example 3 - Tennis dataset: ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No']\n",
      "Gini Impurity: 0.459\n",
      "\n",
      "Tennis Dataset Analysis:\n",
      "Yes: 9/14 = 0.643\n",
      "No: 5/14 = 0.357\n",
      "Manual Gini = 1 - (0.643² + 0.357²)\n",
      "Manual Gini = 1 - (0.413 + 0.128)\n",
      "Manual Gini = 1 - 0.541 = 0.459\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Key Insight: Gini Impurity helps us measure how 'mixed up' our data is.\n",
      "Lower Gini = Better for decision making\n"
     ]
    }
   ],
   "source": [
    "def calculate_gini_impurity(labels):\n",
    "    \"\"\"\n",
    "    Calculate Gini Impurity for a set of labels.\n",
    "    \n",
    "    Args:\n",
    "        labels: List or array of class labels\n",
    "    \n",
    "    Returns:\n",
    "        float: Gini impurity value (0 = pure, higher = more impure)\n",
    "    \"\"\"\n",
    "    if len(labels) == 0:\n",
    "        return 0\n",
    "    \n",
    "    # Count occurrences of each class\n",
    "    unique_classes, counts = np.unique(labels, return_counts=True)\n",
    "    \n",
    "    # Calculate probabilities\n",
    "    probabilities = counts / len(labels)\n",
    "    \n",
    "    # Calculate Gini Impurity: 1 - Σ(pi²)\n",
    "    gini = 1.0 - np.sum(probabilities ** 2)\n",
    "    \n",
    "    return gini\n",
    "\n",
    "# Let's test our Gini calculation with examples\n",
    "print(\"GINI IMPURITY EXAMPLES\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "# Example 1: Perfect purity (all same class)\n",
    "labels1 = ['Yes'] * 10\n",
    "gini1 = calculate_gini_impurity(labels1)\n",
    "print(f\"Example 1 - All 'Yes': {labels1}\")\n",
    "print(f\"Gini Impurity: {gini1:.3f} (Perfect purity!)\")\n",
    "\n",
    "# Example 2: Maximum impurity (50-50 split)\n",
    "labels2 = ['Yes'] * 5 + ['No'] * 5\n",
    "gini2 = calculate_gini_impurity(labels2)\n",
    "print(f\"\\nExample 2 - Equal split: {labels2}\")\n",
    "print(f\"Gini Impurity: {gini2:.3f} (Maximum impurity for binary)\")\n",
    "\n",
    "# Example 3: Our tennis dataset\n",
    "tennis_labels = df_weather['Play_Tennis'].tolist()\n",
    "gini3 = calculate_gini_impurity(tennis_labels)\n",
    "print(f\"\\nExample 3 - Tennis dataset: {tennis_labels}\")\n",
    "print(f\"Gini Impurity: {gini3:.3f}\")\n",
    "\n",
    "# Let's analyze the tennis dataset distribution\n",
    "yes_count = tennis_labels.count('Yes')\n",
    "no_count = tennis_labels.count('No')\n",
    "total = len(tennis_labels)\n",
    "\n",
    "print(f\"\\nTennis Dataset Analysis:\")\n",
    "print(f\"Yes: {yes_count}/{total} = {yes_count/total:.3f}\")\n",
    "print(f\"No: {no_count}/{total} = {no_count/total:.3f}\")\n",
    "print(f\"Manual Gini = 1 - ({yes_count/total:.3f}² + {no_count/total:.3f}²)\")\n",
    "print(f\"Manual Gini = 1 - ({(yes_count/total)**2:.3f} + {(no_count/total)**2:.3f})\")\n",
    "print(f\"Manual Gini = 1 - {(yes_count/total)**2 + (no_count/total)**2:.3f} = {1 - ((yes_count/total)**2 + (no_count/total)**2):.3f}\")\n",
    "\n",
    "# Visualize Gini Impurity for different probability distributions\n",
    "probabilities = np.linspace(0, 1, 100)\n",
    "gini_values = [1 - (p**2 + (1-p)**2) for p in probabilities]\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(probabilities, gini_values, 'b-', linewidth=3, label='Gini Impurity')\n",
    "plt.axhline(y=0.5, color='r', linestyle='--', alpha=0.7, label='Maximum Impurity (0.5)')\n",
    "plt.xlabel('Probability of Class 1')\n",
    "plt.ylabel('Gini Impurity')\n",
    "plt.title('Gini Impurity vs Class Distribution')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Mark special points\n",
    "plt.plot(0, 0, 'ro', markersize=10, label='Pure (Class 0)')\n",
    "plt.plot(1, 0, 'go', markersize=10, label='Pure (Class 1)')\n",
    "plt.plot(0.5, 0.5, 'yo', markersize=10, label='Maximum Impurity')\n",
    "\n",
    "# Add annotations\n",
    "plt.annotate('Pure\\n(Gini=0)', xy=(0, 0), xytext=(0.15, 0.1),\n",
    "            arrowprops=dict(arrowstyle='->', color='red'), fontsize=10)\n",
    "plt.annotate('Pure\\n(Gini=0)', xy=(1, 0), xytext=(0.85, 0.1),\n",
    "            arrowprops=dict(arrowstyle='->', color='green'), fontsize=10)\n",
    "plt.annotate('Most Impure\\n(Gini=0.5)', xy=(0.5, 0.5), xytext=(0.3, 0.4),\n",
    "            arrowprops=dict(arrowstyle='->', color='orange'), fontsize=10)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "# Pie charts showing different impurity levels\n",
    "scenarios = [\n",
    "    ([10, 0], \"Pure (Gini=0.0)\"),\n",
    "    ([9, 1], \"Low Impurity (Gini=0.18)\"), \n",
    "    ([7, 3], \"Medium Impurity (Gini=0.42)\"),\n",
    "    ([5, 5], \"Max Impurity (Gini=0.5)\")\n",
    "]\n",
    "\n",
    "for i, (counts, title) in enumerate(scenarios):\n",
    "    plt.subplot(2, 4, i+5)\n",
    "    labels = ['Class A', 'Class B']\n",
    "    colors = ['lightblue', 'lightcoral']\n",
    "    \n",
    "    # Only show non-zero counts\n",
    "    non_zero_counts = [c for c in counts if c > 0]\n",
    "    non_zero_labels = [labels[j] for j, c in enumerate(counts) if c > 0]\n",
    "    non_zero_colors = [colors[j] for j, c in enumerate(counts) if c > 0]\n",
    "    \n",
    "    plt.pie(non_zero_counts, labels=non_zero_labels, colors=non_zero_colors, autopct='%1.0f%%')\n",
    "    gini = calculate_gini_impurity(['A']*counts[0] + ['B']*counts[1])\n",
    "    plt.title(f\"{title}\\nGini: {gini:.2f}\", fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nKey Insight: Gini Impurity helps us measure how 'mixed up' our data is.\")\n",
    "print(f\"Lower Gini = Better for decision making\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968ef020",
   "metadata": {},
   "source": [
    "## Information Gain: Choosing the Best Split\n",
    "\n",
    "**What is Information Gain?**\n",
    "Information Gain measures how much a split improves the purity of our data. It's the reduction in Gini Impurity after splitting.\n",
    "\n",
    "**Formula:**\n",
    "```\n",
    "Information Gain = Gini(parent) - Σ(|child_i|/|parent| × Gini(child_i))\n",
    "```\n",
    "\n",
    "**Process:**\n",
    "1. Calculate Gini Impurity of parent node\n",
    "2. Split data based on a feature value\n",
    "3. Calculate weighted Gini of child nodes\n",
    "4. Information Gain = Parent Gini - Weighted Child Gini\n",
    "\n",
    "**Goal:** Choose the split that gives the **highest Information Gain** (biggest reduction in impurity)\n",
    "\n",
    "Let's see this in action with our tennis dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b6e9fc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANALYZING SPLITS FOR TENNIS DATASET\n",
      "============================================================\n",
      "Original dataset distribution:\n",
      "Play_Tennis\n",
      "Yes    9\n",
      "No     5\n",
      "Name: count, dtype: int64\n",
      "Original Gini: 0.459\n",
      "\n",
      "============================================================\n",
      "TESTING DIFFERENT SPLITS\n",
      "============================================================\n",
      "\n",
      "1. SPLITTING BY TEMPERATURE\n",
      "----------------------------------------\n",
      "Temperature = Cool  :  5 samples, Yes:3, No:2, Gini: 0.480\n",
      "Temperature = Hot   :  4 samples, Yes:2, No:2, Gini: 0.500\n",
      "Temperature = Mild  :  5 samples, Yes:4, No:1, Gini: 0.320\n",
      "\n",
      "Split: Temperature = 'Hot' vs Others\n",
      "  Left (Hot): 4 samples, Gini: 0.500\n",
      "  Right (Others): 10 samples, Gini: 0.420\n",
      "  Information Gain: 0.016\n",
      "\n",
      "Split: Temperature = 'Mild' vs Others\n",
      "  Left (Mild): 5 samples, Gini: 0.320\n",
      "  Right (Others): 9 samples, Gini: 0.494\n",
      "  Information Gain: 0.027\n",
      "\n",
      "Split: Temperature = 'Cool' vs Others\n",
      "  Left (Cool): 5 samples, Gini: 0.480\n",
      "  Right (Others): 9 samples, Gini: 0.444\n",
      "  Information Gain: 0.002\n",
      "\n",
      "Best Temperature split: 'Mild' with gain: 0.027\n",
      "\n",
      "\n",
      "2. SPLITTING BY HUMIDITY\n",
      "----------------------------------------\n",
      "Humidity = High  :  7 samples, Yes:3, No:4, Gini: 0.490\n",
      "Humidity = Normal:  7 samples, Yes:6, No:1, Gini: 0.245\n",
      "\n",
      "Split: Humidity = 'High' vs Others\n",
      "  Left (High): 7 samples, Gini: 0.490\n",
      "  Right (Others): 7 samples, Gini: 0.245\n",
      "  Information Gain: 0.092\n",
      "\n",
      "Split: Humidity = 'Normal' vs Others\n",
      "  Left (Normal): 7 samples, Gini: 0.245\n",
      "  Right (Others): 7 samples, Gini: 0.490\n",
      "  Information Gain: 0.092\n",
      "\n",
      "Best Humidity split: 'High' with gain: 0.092\n",
      "\n",
      "\n",
      "3. SPLITTING BY WIND\n",
      "----------------------------------------\n",
      "Wind = Strong:  6 samples, Yes:3, No:3, Gini: 0.500\n",
      "Wind = Weak  :  8 samples, Yes:6, No:2, Gini: 0.375\n",
      "\n",
      "Split: Wind = 'Weak' vs Others\n",
      "  Left (Weak): 8 samples, Gini: 0.375\n",
      "  Right (Others): 6 samples, Gini: 0.500\n",
      "  Information Gain: 0.031\n",
      "\n",
      "Split: Wind = 'Strong' vs Others\n",
      "  Left (Strong): 6 samples, Gini: 0.500\n",
      "  Right (Others): 8 samples, Gini: 0.375\n",
      "  Information Gain: 0.031\n",
      "\n",
      "Best Wind split: 'Weak' with gain: 0.031\n",
      "\n",
      "============================================================\n",
      "BEST SPLIT COMPARISON\n",
      "============================================================\n",
      "Temperature: Mild         → Gain: 0.027\n",
      "Humidity:    High         → Gain: 0.092\n",
      "Wind:        Weak         → Gain: 0.031\n",
      "\n",
      "WINNER: Humidity = 'High' with Information Gain: 0.092\n",
      "This should be our ROOT NODE split.\n"
     ]
    }
   ],
   "source": [
    "def calculate_information_gain(parent_labels, left_labels, right_labels):\n",
    "    \"\"\"\n",
    "    Calculate Information Gain from a split.\n",
    "    \n",
    "    Args:\n",
    "        parent_labels: Labels before split\n",
    "        left_labels: Labels in left child after split  \n",
    "        right_labels: Labels in right child after split\n",
    "    \n",
    "    Returns:\n",
    "        float: Information gain value\n",
    "    \"\"\"\n",
    "    # Calculate parent Gini\n",
    "    parent_gini = calculate_gini_impurity(parent_labels)\n",
    "    \n",
    "    # Calculate weighted Gini of children\n",
    "    total_samples = len(parent_labels)\n",
    "    left_weight = len(left_labels) / total_samples\n",
    "    right_weight = len(right_labels) / total_samples\n",
    "    \n",
    "    left_gini = calculate_gini_impurity(left_labels)\n",
    "    right_gini = calculate_gini_impurity(right_labels)\n",
    "    \n",
    "    weighted_child_gini = (left_weight * left_gini) + (right_weight * right_gini)\n",
    "    \n",
    "    # Information Gain = Parent Gini - Weighted Child Gini\n",
    "    information_gain = parent_gini - weighted_child_gini\n",
    "    \n",
    "    return information_gain, parent_gini, weighted_child_gini, left_gini, right_gini\n",
    "\n",
    "# Let's analyze splits for our tennis dataset\n",
    "print(\"ANALYZING SPLITS FOR TENNIS DATASET\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# First, let's see the distribution of our target variable\n",
    "target = df_weather['Play_Tennis']\n",
    "print(f\"Original dataset distribution:\")\n",
    "print(target.value_counts())\n",
    "print(f\"Original Gini: {calculate_gini_impurity(target):.3f}\")\n",
    "\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"TESTING DIFFERENT SPLITS\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Test splitting by Temperature\n",
    "print(\"\\n1. SPLITTING BY TEMPERATURE\")\n",
    "print(\"-\" * 40)\n",
    "\n",
    "temp_groups = df_weather.groupby('Temperature')\n",
    "for temp_value, group in temp_groups:\n",
    "    labels = group['Play_Tennis'].tolist()\n",
    "    gini = calculate_gini_impurity(labels)\n",
    "    count = len(labels)\n",
    "    yes_count = labels.count('Yes')\n",
    "    no_count = labels.count('No')\n",
    "    print(f\"Temperature = {temp_value:6}: {count:2} samples, Yes:{yes_count}, No:{no_count}, Gini: {gini:.3f}\")\n",
    "\n",
    "# Calculate information gain for Temperature splits\n",
    "# For categorical variables, we test each value as a split\n",
    "best_temp_gain = 0\n",
    "best_temp_split = None\n",
    "\n",
    "for temp_value in df_weather['Temperature'].unique():\n",
    "    # Split: this temperature vs others\n",
    "    mask = df_weather['Temperature'] == temp_value\n",
    "    left_labels = df_weather[mask]['Play_Tennis'].tolist()\n",
    "    right_labels = df_weather[~mask]['Play_Tennis'].tolist()\n",
    "    \n",
    "    if len(left_labels) > 0 and len(right_labels) > 0:  # Valid split\n",
    "        gain, parent_gini, weighted_child, left_gini, right_gini = calculate_information_gain(\n",
    "            target.tolist(), left_labels, right_labels\n",
    "        )\n",
    "        \n",
    "        print(f\"\\nSplit: Temperature = '{temp_value}' vs Others\")\n",
    "        print(f\"  Left ({temp_value}): {len(left_labels)} samples, Gini: {left_gini:.3f}\")\n",
    "        print(f\"  Right (Others): {len(right_labels)} samples, Gini: {right_gini:.3f}\")\n",
    "        print(f\"  Information Gain: {gain:.3f}\")\n",
    "        \n",
    "        if gain > best_temp_gain:\n",
    "            best_temp_gain = gain\n",
    "            best_temp_split = temp_value\n",
    "\n",
    "print(f\"\\nBest Temperature split: '{best_temp_split}' with gain: {best_temp_gain:.3f}\")\n",
    "\n",
    "# Test splitting by Humidity\n",
    "print(\"\\n\\n2. SPLITTING BY HUMIDITY\")\n",
    "print(\"-\" * 40)\n",
    "\n",
    "humidity_groups = df_weather.groupby('Humidity')\n",
    "for humidity_value, group in humidity_groups:\n",
    "    labels = group['Play_Tennis'].tolist()\n",
    "    gini = calculate_gini_impurity(labels)\n",
    "    count = len(labels)\n",
    "    yes_count = labels.count('Yes')\n",
    "    no_count = labels.count('No')\n",
    "    print(f\"Humidity = {humidity_value:6}: {count:2} samples, Yes:{yes_count}, No:{no_count}, Gini: {gini:.3f}\")\n",
    "\n",
    "best_humidity_gain = 0\n",
    "best_humidity_split = None\n",
    "\n",
    "for humidity_value in df_weather['Humidity'].unique():\n",
    "    mask = df_weather['Humidity'] == humidity_value\n",
    "    left_labels = df_weather[mask]['Play_Tennis'].tolist()\n",
    "    right_labels = df_weather[~mask]['Play_Tennis'].tolist()\n",
    "    \n",
    "    if len(left_labels) > 0 and len(right_labels) > 0:\n",
    "        gain, parent_gini, weighted_child, left_gini, right_gini = calculate_information_gain(\n",
    "            target.tolist(), left_labels, right_labels\n",
    "        )\n",
    "        \n",
    "        print(f\"\\nSplit: Humidity = '{humidity_value}' vs Others\")\n",
    "        print(f\"  Left ({humidity_value}): {len(left_labels)} samples, Gini: {left_gini:.3f}\")\n",
    "        print(f\"  Right (Others): {len(right_labels)} samples, Gini: {right_gini:.3f}\")\n",
    "        print(f\"  Information Gain: {gain:.3f}\")\n",
    "        \n",
    "        if gain > best_humidity_gain:\n",
    "            best_humidity_gain = gain\n",
    "            best_humidity_split = humidity_value\n",
    "\n",
    "print(f\"\\nBest Humidity split: '{best_humidity_split}' with gain: {best_humidity_gain:.3f}\")\n",
    "\n",
    "# Test splitting by Wind\n",
    "print(\"\\n\\n3. SPLITTING BY WIND\")\n",
    "print(\"-\" * 40)\n",
    "\n",
    "wind_groups = df_weather.groupby('Wind')\n",
    "for wind_value, group in wind_groups:\n",
    "    labels = group['Play_Tennis'].tolist()\n",
    "    gini = calculate_gini_impurity(labels)\n",
    "    count = len(labels)\n",
    "    yes_count = labels.count('Yes')\n",
    "    no_count = labels.count('No')\n",
    "    print(f\"Wind = {wind_value:6}: {count:2} samples, Yes:{yes_count}, No:{no_count}, Gini: {gini:.3f}\")\n",
    "\n",
    "best_wind_gain = 0\n",
    "best_wind_split = None\n",
    "\n",
    "for wind_value in df_weather['Wind'].unique():\n",
    "    mask = df_weather['Wind'] == wind_value\n",
    "    left_labels = df_weather[mask]['Play_Tennis'].tolist()\n",
    "    right_labels = df_weather[~mask]['Play_Tennis'].tolist()\n",
    "    \n",
    "    if len(left_labels) > 0 and len(right_labels) > 0:\n",
    "        gain, parent_gini, weighted_child, left_gini, right_gini = calculate_information_gain(\n",
    "            target.tolist(), left_labels, right_labels\n",
    "        )\n",
    "        \n",
    "        print(f\"\\nSplit: Wind = '{wind_value}' vs Others\")\n",
    "        print(f\"  Left ({wind_value}): {len(left_labels)} samples, Gini: {left_gini:.3f}\")\n",
    "        print(f\"  Right (Others): {len(right_labels)} samples, Gini: {right_gini:.3f}\")\n",
    "        print(f\"  Information Gain: {gain:.3f}\")\n",
    "        \n",
    "        if gain > best_wind_gain:\n",
    "            best_wind_gain = gain\n",
    "            best_wind_split = wind_value\n",
    "\n",
    "print(f\"\\nBest Wind split: '{best_wind_split}' with gain: {best_wind_gain:.3f}\")\n",
    "\n",
    "# Summary of best splits\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"BEST SPLIT COMPARISON\")\n",
    "print(\"=\" * 60)\n",
    "print(f\"Temperature: {best_temp_split:12} → Gain: {best_temp_gain:.3f}\")\n",
    "print(f\"Humidity:    {best_humidity_split:12} → Gain: {best_humidity_gain:.3f}\")\n",
    "print(f\"Wind:        {best_wind_split:12} → Gain: {best_wind_gain:.3f}\")\n",
    "\n",
    "# Find the overall best split\n",
    "splits = [\n",
    "    ('Temperature', best_temp_split, best_temp_gain),\n",
    "    ('Humidity', best_humidity_split, best_humidity_gain), \n",
    "    ('Wind', best_wind_split, best_wind_gain)\n",
    "]\n",
    "\n",
    "best_feature, best_value, best_gain = max(splits, key=lambda x: x[2])\n",
    "print(f\"\\nWINNER: {best_feature} = '{best_value}' with Information Gain: {best_gain:.3f}\")\n",
    "print(f\"This should be our ROOT NODE split.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ddf314",
   "metadata": {},
   "source": [
    "## Building a Decision Tree from Scratch\n",
    "\n",
    "Now let's implement a complete decision tree algorithm! We'll build it step by step to understand every component.\n",
    "\n",
    "**Algorithm Steps:**\n",
    "1. **Start** with all training data at root\n",
    "2. **Find** the best split (highest information gain)\n",
    "3. **Split** data into child nodes\n",
    "4. **Repeat** process for each child (recursively)\n",
    "5. **Stop** when node is pure or meets stopping criteria\n",
    "6. **Predict** using majority class in leaf nodes\n",
    "\n",
    "Let's code this up!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0dba499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING OUR DECISION TREE FROM SCRATCH\n",
      "============================================================\n",
      "DECISION TREE STRUCTURE:\n",
      "----------------------------------------\n",
      "Root: Humidity = 'High'? (Gini: 0.459, Samples: 14, Classes: {'No': np.int64(5), 'Yes': np.int64(9)})\n",
      "  Yes: Temperature = 'Mild'? (Gini: 0.490, Samples: 7, Classes: {'No': np.int64(4), 'Yes': np.int64(3)})\n",
      "    Yes: Wind = 'Strong'? (Gini: 0.375, Samples: 4, Classes: {'No': np.int64(1), 'Yes': np.int64(3)})\n",
      "      Yes: Predict 'Yes' (Gini: 0.000, Samples: 1, Classes: {'Yes': np.int64(1)})\n",
      "      No: Predict 'Yes' (Gini: 0.444, Samples: 3, Classes: {'No': np.int64(1), 'Yes': np.int64(2)})\n",
      "    No: Predict 'No' (Gini: 0.000, Samples: 3, Classes: {'No': np.int64(3)})\n",
      "  No: Wind = 'Strong'? (Gini: 0.245, Samples: 7, Classes: {'No': np.int64(1), 'Yes': np.int64(6)})\n",
      "    Yes: Temperature = 'Cool'? (Gini: 0.444, Samples: 3, Classes: {'No': np.int64(1), 'Yes': np.int64(2)})\n",
      "      Yes: Predict 'No' (Gini: 0.500, Samples: 2, Classes: {'No': np.int64(1), 'Yes': np.int64(1)})\n",
      "      No: Predict 'Yes' (Gini: 0.000, Samples: 1, Classes: {'Yes': np.int64(1)})\n",
      "    No: Predict 'Yes' (Gini: 0.000, Samples: 4, Classes: {'Yes': np.int64(4)})\n",
      "\n",
      "TRAINING RESULTS:\n",
      "Training Accuracy: 0.857 (85.7%)\n",
      "\n",
      "PREDICTION DETAILS:\n",
      "Index | Temp    | Humidity | Wind   | Actual | Predicted | Correct?\n",
      "----------------------------------------------------------------------\n",
      "    0 | Hot     | High     | Weak   | No     | No        | ✓\n",
      "    1 | Hot     | High     | Strong | No     | No        | ✓\n",
      "    2 | Hot     | Normal   | Weak   | Yes    | Yes       | ✓\n",
      "    3 | Mild    | High     | Weak   | Yes    | Yes       | ✓\n",
      "    4 | Cool    | Normal   | Weak   | Yes    | Yes       | ✓\n",
      "    5 | Cool    | Normal   | Strong | No     | No        | ✓\n",
      "    6 | Cool    | Normal   | Strong | Yes    | No        | ✗\n",
      "    7 | Mild    | High     | Weak   | No     | Yes       | ✗\n",
      "    8 | Cool    | Normal   | Weak   | Yes    | Yes       | ✓\n",
      "    9 | Mild    | Normal   | Weak   | Yes    | Yes       | ✓\n",
      "   10 | Mild    | High     | Strong | Yes    | Yes       | ✓\n",
      "   11 | Hot     | Normal   | Strong | Yes    | Yes       | ✓\n",
      "   12 | Mild    | High     | Weak   | Yes    | Yes       | ✓\n",
      "   13 | Cool    | High     | Strong | No     | No        | ✓\n",
      "\n",
      "Our tree correctly classifies 12/14 training examples.\n"
     ]
    }
   ],
   "source": [
    "class TreeNode:\n",
    "    \"\"\"A node in our decision tree.\"\"\"\n",
    "    def __init__(self):\n",
    "        self.feature = None          # Feature to split on\n",
    "        self.value = None           # Value to split on\n",
    "        self.left = None            # Left child node\n",
    "        self.right = None           # Right child node\n",
    "        self.prediction = None      # Prediction for leaf nodes\n",
    "        self.gini = None           # Gini impurity of this node\n",
    "        self.samples = 0           # Number of samples in this node\n",
    "        self.class_counts = {}     # Count of each class\n",
    "        \n",
    "    def is_leaf(self):\n",
    "        \"\"\"Check if this is a leaf node.\"\"\"\n",
    "        return self.prediction is not None\n",
    "\n",
    "class DecisionTreeClassifierFromScratch:\n",
    "    \"\"\"Decision Tree implementation from scratch using Gini Impurity.\"\"\"\n",
    "    \n",
    "    def __init__(self, max_depth=10, min_samples_split=2, min_samples_leaf=1):\n",
    "        self.max_depth = max_depth\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.min_samples_leaf = min_samples_leaf\n",
    "        self.root = None\n",
    "        self.feature_names = None\n",
    "        \n",
    "    def fit(self, X, y, feature_names=None):\n",
    "        \"\"\"Train the decision tree.\"\"\"\n",
    "        self.feature_names = feature_names if feature_names else [f'feature_{i}' for i in range(X.shape[1])]\n",
    "        self.root = self._build_tree(X, y, depth=0)\n",
    "        \n",
    "    def _build_tree(self, X, y, depth):\n",
    "        \"\"\"Recursively build the decision tree.\"\"\"\n",
    "        node = TreeNode()\n",
    "        node.samples = len(y)\n",
    "        node.gini = calculate_gini_impurity(y)\n",
    "        \n",
    "        # Count classes in this node\n",
    "        unique_classes, counts = np.unique(y, return_counts=True)\n",
    "        node.class_counts = dict(zip(unique_classes, counts))\n",
    "        \n",
    "        # Stopping criteria\n",
    "        if (depth >= self.max_depth or \n",
    "            len(y) < self.min_samples_split or \n",
    "            node.gini == 0 or  # Pure node\n",
    "            len(unique_classes) == 1):  # Only one class\n",
    "            \n",
    "            # Make this a leaf node\n",
    "            node.prediction = max(node.class_counts, key=node.class_counts.get)\n",
    "            return node\n",
    "        \n",
    "        # Find the best split\n",
    "        best_gain = 0\n",
    "        best_feature = None\n",
    "        best_value = None\n",
    "        best_left_indices = None\n",
    "        best_right_indices = None\n",
    "        \n",
    "        # Try each feature\n",
    "        for feature_idx in range(X.shape[1]):\n",
    "            # For categorical features, try each unique value\n",
    "            unique_values = np.unique(X[:, feature_idx])\n",
    "            \n",
    "            for value in unique_values:\n",
    "                # Split data\n",
    "                left_indices = X[:, feature_idx] == value\n",
    "                right_indices = ~left_indices\n",
    "                \n",
    "                # Skip if split doesn't create valid children\n",
    "                if (np.sum(left_indices) < self.min_samples_leaf or \n",
    "                    np.sum(right_indices) < self.min_samples_leaf):\n",
    "                    continue\n",
    "                \n",
    "                # Calculate information gain\n",
    "                left_labels = y[left_indices]\n",
    "                right_labels = y[right_indices]\n",
    "                \n",
    "                if len(left_labels) == 0 or len(right_labels) == 0:\n",
    "                    continue\n",
    "                \n",
    "                gain, _, _, _, _ = calculate_information_gain(y, left_labels, right_labels)\n",
    "                \n",
    "                # Update best split if this is better\n",
    "                if gain > best_gain:\n",
    "                    best_gain = gain\n",
    "                    best_feature = feature_idx\n",
    "                    best_value = value\n",
    "                    best_left_indices = left_indices\n",
    "                    best_right_indices = right_indices\n",
    "        \n",
    "        # If no good split found, make leaf\n",
    "        if best_gain == 0:\n",
    "            node.prediction = max(node.class_counts, key=node.class_counts.get)\n",
    "            return node\n",
    "        \n",
    "        # Create split\n",
    "        node.feature = best_feature\n",
    "        node.value = best_value\n",
    "        \n",
    "        # Recursively build children\n",
    "        node.left = self._build_tree(X[best_left_indices], y[best_left_indices], depth + 1)\n",
    "        node.right = self._build_tree(X[best_right_indices], y[best_right_indices], depth + 1)\n",
    "        \n",
    "        return node\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Make predictions for new data.\"\"\"\n",
    "        return np.array([self._predict_sample(sample, self.root) for sample in X])\n",
    "    \n",
    "    def _predict_sample(self, sample, node):\n",
    "        \"\"\"Predict class for a single sample.\"\"\"\n",
    "        if node.is_leaf():\n",
    "            return node.prediction\n",
    "        \n",
    "        # Navigate tree based on feature value\n",
    "        if sample[node.feature] == node.value:\n",
    "            return self._predict_sample(sample, node.left)\n",
    "        else:\n",
    "            return self._predict_sample(sample, node.right)\n",
    "    \n",
    "    def print_tree(self, node=None, depth=0, prefix=\"Root\"):\n",
    "        \"\"\"Print the decision tree structure.\"\"\"\n",
    "        if node is None:\n",
    "            node = self.root\n",
    "        \n",
    "        # Indentation for tree structure\n",
    "        indent = \"  \" * depth\n",
    "        \n",
    "        if node.is_leaf():\n",
    "            print(f\"{indent}{prefix}: Predict '{node.prediction}' \"\n",
    "                  f\"(Gini: {node.gini:.3f}, Samples: {node.samples}, \"\n",
    "                  f\"Classes: {node.class_counts})\")\n",
    "        else:\n",
    "            feature_name = self.feature_names[node.feature]\n",
    "            print(f\"{indent}{prefix}: {feature_name} = '{node.value}'? \"\n",
    "                  f\"(Gini: {node.gini:.3f}, Samples: {node.samples}, \"\n",
    "                  f\"Classes: {node.class_counts})\")\n",
    "            \n",
    "            # Print children\n",
    "            self.print_tree(node.left, depth + 1, f\"Yes\")\n",
    "            self.print_tree(node.right, depth + 1, f\"No\")\n",
    "\n",
    "# Prepare our tennis data for training\n",
    "# Convert categorical to numerical encoding\n",
    "def encode_categorical_data(df):\n",
    "    \"\"\"Convert categorical data to numerical encoding.\"\"\"\n",
    "    encoded_df = df.copy()\n",
    "    label_encoders = {}\n",
    "    \n",
    "    for column in encoded_df.columns:\n",
    "        if encoded_df[column].dtype == 'object':\n",
    "            unique_values = encoded_df[column].unique()\n",
    "            label_encoders[column] = {val: idx for idx, val in enumerate(unique_values)}\n",
    "            encoded_df[column] = encoded_df[column].map(label_encoders[column])\n",
    "    \n",
    "    return encoded_df, label_encoders\n",
    "\n",
    "# For our tree, we'll work with original categorical data\n",
    "# Convert to numpy arrays for our custom implementation\n",
    "feature_columns = ['Temperature', 'Humidity', 'Wind']\n",
    "X_tennis = df_weather[feature_columns].values\n",
    "y_tennis = df_weather['Play_Tennis'].values\n",
    "\n",
    "print(\"TRAINING OUR DECISION TREE FROM SCRATCH\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Create and train our tree\n",
    "tree = DecisionTreeClassifierFromScratch(max_depth=5, min_samples_split=2, min_samples_leaf=1)\n",
    "tree.fit(X_tennis, y_tennis, feature_names=feature_columns)\n",
    "\n",
    "print(\"DECISION TREE STRUCTURE:\")\n",
    "print(\"-\" * 40)\n",
    "tree.print_tree()\n",
    "\n",
    "# Test our tree with the training data\n",
    "predictions = tree.predict(X_tennis)\n",
    "accuracy = np.mean(predictions == y_tennis)\n",
    "\n",
    "print(f\"\\nTRAINING RESULTS:\")\n",
    "print(f\"Training Accuracy: {accuracy:.3f} ({accuracy*100:.1f}%)\")\n",
    "\n",
    "# Show predictions vs actual\n",
    "print(f\"\\nPREDICTION DETAILS:\")\n",
    "print(\"Index | Temp    | Humidity | Wind   | Actual | Predicted | Correct?\")\n",
    "print(\"-\" * 70)\n",
    "for i in range(len(X_tennis)):\n",
    "    temp, humidity, wind = X_tennis[i]\n",
    "    actual = y_tennis[i] \n",
    "    predicted = predictions[i]\n",
    "    correct = \"✓\" if actual == predicted else \"✗\"\n",
    "    print(f\"{i:5d} | {temp:7} | {humidity:8} | {wind:6} | {actual:6} | {predicted:9} | {correct}\")\n",
    "\n",
    "print(f\"\\nOur tree correctly classifies {np.sum(predictions == y_tennis)}/{len(y_tennis)} training examples.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a535c2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VISUALIZING OUR DECISION TREE\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "🔍 EXAMPLE DECISION TRACES\n",
      "============================================================\n",
      "\n",
      "TRACING DECISION PATH\n",
      "Sample: {'Temperature': 'Hot', 'Humidity': 'High', 'Wind': 'Weak'}\n",
      "--------------------------------------------------\n",
      "Step 1: Humidity = 'High'? → Yes\n",
      "Step 2: Temperature = 'Mild'? → No\n",
      "\n",
      "🎯 Final Prediction: No\n",
      "   Node Details: Gini=0.000, Samples=3\n",
      "   Class Distribution: {'No': np.int64(3)}\n",
      "\n",
      "TRACING DECISION PATH\n",
      "Sample: {'Temperature': 'Cool', 'Humidity': 'Normal', 'Wind': 'Weak'}\n",
      "--------------------------------------------------\n",
      "Step 1: Humidity = 'High'? → No\n",
      "Step 2: Wind = 'Strong'? → No\n",
      "\n",
      "🎯 Final Prediction: Yes\n",
      "   Node Details: Gini=0.000, Samples=4\n",
      "   Class Distribution: {'Yes': np.int64(4)}\n",
      "\n",
      "========================================\n",
      "TESTING NEW EXAMPLE\n",
      "========================================\n",
      "\n",
      "TRACING DECISION PATH\n",
      "Sample: {'Temperature': np.str_('Mild'), 'Humidity': np.str_('Normal'), 'Wind': np.str_('Strong')}\n",
      "--------------------------------------------------\n",
      "Step 1: Humidity = 'High'? → No\n",
      "Step 2: Wind = 'Strong'? → Yes\n",
      "Step 3: Temperature = 'Cool'? → No\n",
      "\n",
      "🎯 Final Prediction: Yes\n",
      "   Node Details: Gini=0.000, Samples=1\n",
      "   Class Distribution: {'Yes': np.int64(1)}\n"
     ]
    }
   ],
   "source": [
    "# Create a visual representation of our decision tree\n",
    "def visualize_tree_structure(tree):\n",
    "    \"\"\"Create a visual representation of the decision tree.\"\"\"\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))\n",
    "    \n",
    "    # Left plot: Tree structure as text with boxes\n",
    "    ax1.set_xlim(0, 10)\n",
    "    ax1.set_ylim(0, 10)\n",
    "    ax1.set_title(\"Decision Tree Structure\", fontsize=16, weight='bold')\n",
    "    ax1.axis('off')\n",
    "    \n",
    "    def draw_node(node, x, y, width, depth=0):\n",
    "        \"\"\"Recursively draw tree nodes.\"\"\"\n",
    "        if node is None:\n",
    "            return\n",
    "        \n",
    "        # Node styling\n",
    "        if node.is_leaf():\n",
    "            box_color = 'lightgreen' if node.prediction == 'Yes' else 'lightcoral'\n",
    "            text = f\"PREDICT: {node.prediction}\\nGini: {node.gini:.3f}\\nSamples: {node.samples}\"\n",
    "        else:\n",
    "            box_color = 'lightblue'\n",
    "            feature_name = tree.feature_names[node.feature]\n",
    "            text = f\"{feature_name} = '{node.value}'?\\nGini: {node.gini:.3f}\\nSamples: {node.samples}\"\n",
    "        \n",
    "        # Draw box\n",
    "        box = plt.Rectangle((x-width/2, y-0.5), width, 1, \n",
    "                           facecolor=box_color, edgecolor='black', linewidth=1)\n",
    "        ax1.add_patch(box)\n",
    "        \n",
    "        # Add text\n",
    "        ax1.text(x, y, text, ha='center', va='center', fontsize=8, weight='bold')\n",
    "        \n",
    "        # Draw children\n",
    "        if not node.is_leaf():\n",
    "            child_width = width * 0.7\n",
    "            child_y = y - 2\n",
    "            left_x = x - width * 0.6\n",
    "            right_x = x + width * 0.6\n",
    "            \n",
    "            # Draw lines to children\n",
    "            ax1.plot([x, left_x], [y-0.5, child_y+0.5], 'k-', linewidth=1)\n",
    "            ax1.plot([x, right_x], [y-0.5, child_y+0.5], 'k-', linewidth=1)\n",
    "            \n",
    "            # Add Yes/No labels\n",
    "            ax1.text(left_x + 0.3, y - 1, 'Yes', ha='center', va='center', \n",
    "                    fontsize=10, weight='bold', color='green')\n",
    "            ax1.text(right_x - 0.3, y - 1, 'No', ha='center', va='center', \n",
    "                    fontsize=10, weight='bold', color='red')\n",
    "            \n",
    "            # Recursively draw children\n",
    "            draw_node(node.left, left_x, child_y, child_width, depth+1)\n",
    "            draw_node(node.right, right_x, child_y, child_width, depth+1)\n",
    "    \n",
    "    # Draw the tree starting from root\n",
    "    draw_node(tree.root, 5, 8.5, 4)\n",
    "    \n",
    "    # Right plot: Feature importance (based on how often features are used)\n",
    "    feature_usage = {name: 0 for name in tree.feature_names}\n",
    "    \n",
    "    def count_feature_usage(node):\n",
    "        if node and not node.is_leaf():\n",
    "            feature_name = tree.feature_names[node.feature]\n",
    "            feature_usage[feature_name] += 1\n",
    "            count_feature_usage(node.left)\n",
    "            count_feature_usage(node.right)\n",
    "    \n",
    "    count_feature_usage(tree.root)\n",
    "    \n",
    "    features = list(feature_usage.keys())\n",
    "    usage_counts = list(feature_usage.values())\n",
    "    \n",
    "    bars = ax2.bar(features, usage_counts, color=['skyblue', 'lightgreen', 'lightcoral'])\n",
    "    ax2.set_title('Feature Usage in Tree', fontsize=16, weight='bold')\n",
    "    ax2.set_ylabel('Number of Splits')\n",
    "    ax2.set_xlabel('Features')\n",
    "    \n",
    "    # Add value labels on bars\n",
    "    for bar, count in zip(bars, usage_counts):\n",
    "        if count > 0:\n",
    "            ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.05, \n",
    "                    str(count), ha='center', va='bottom', fontsize=12, weight='bold')\n",
    "    \n",
    "    ax2.grid(True, alpha=0.3)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Visualize our tree\n",
    "print(\"VISUALIZING OUR DECISION TREE\")\n",
    "print(\"=\" * 50)\n",
    "visualize_tree_structure(tree)\n",
    "\n",
    "# Let's also create a step-by-step decision process visualization\n",
    "def trace_decision_path(tree, sample, feature_names):\n",
    "    \"\"\"Trace the decision path for a specific sample.\"\"\"\n",
    "    print(f\"\\nTRACING DECISION PATH\")\n",
    "    print(f\"Sample: {dict(zip(feature_names, sample))}\")\n",
    "    print(\"-\" * 50)\n",
    "    \n",
    "    node = tree.root\n",
    "    path = []\n",
    "    \n",
    "    while not node.is_leaf():\n",
    "        feature_name = feature_names[node.feature]\n",
    "        feature_value = sample[node.feature]\n",
    "        \n",
    "        if feature_value == node.value:\n",
    "            decision = \"Yes\"\n",
    "            path.append(f\"{feature_name} = '{node.value}'? → {decision}\")\n",
    "            node = node.left\n",
    "        else:\n",
    "            decision = \"No\"\n",
    "            path.append(f\"{feature_name} = '{node.value}'? → {decision}\")\n",
    "            node = node.right\n",
    "    \n",
    "    # Print the path\n",
    "    for i, step in enumerate(path, 1):\n",
    "        print(f\"Step {i}: {step}\")\n",
    "    \n",
    "    print(f\"\\n🎯 Final Prediction: {node.prediction}\")\n",
    "    print(f\"   Node Details: Gini={node.gini:.3f}, Samples={node.samples}\")\n",
    "    print(f\"   Class Distribution: {node.class_counts}\")\n",
    "    \n",
    "    return node.prediction\n",
    "\n",
    "# Trace a few examples\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"🔍 EXAMPLE DECISION TRACES\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Example 1: First sample from training data\n",
    "example1 = X_tennis[0]  # ['Hot', 'High', 'Weak']\n",
    "trace_decision_path(tree, example1, feature_columns)\n",
    "\n",
    "# Example 2: Different sample\n",
    "example2 = X_tennis[4]  # ['Cool', 'Normal', 'Weak'] \n",
    "trace_decision_path(tree, example2, feature_columns)\n",
    "\n",
    "# Example 3: Let's try a new unseen example\n",
    "print(\"\\n\" + \"=\" * 40)\n",
    "print(\"TESTING NEW EXAMPLE\")\n",
    "print(\"=\" * 40)\n",
    "# Create a new sample: ['Mild', 'Normal', 'Strong']\n",
    "new_sample = np.array(['Mild', 'Normal', 'Strong'])\n",
    "prediction = trace_decision_path(tree, new_sample, feature_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cc80a5",
   "metadata": {},
   "source": [
    "## Comparing with Scikit-Learn\n",
    "\n",
    "Now let's compare our implementation with scikit-learn's decision tree to validate our understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f34ce847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCIKIT-LEARN VS OUR IMPLEMENTATION\n",
      "============================================================\n",
      "PREDICTION COMPARISON:\n",
      "Index | Our Tree | Scikit-Learn | Actual | Match?\n",
      "--------------------------------------------------\n",
      "    0 | No       | No           | No     | ✓\n",
      "    1 | No       | No           | No     | ✓\n",
      "    2 | Yes      | Yes          | Yes    | ✓\n",
      "    3 | Yes      | Yes          | Yes    | ✓\n",
      "    4 | Yes      | Yes          | Yes    | ✓\n",
      "    5 | No       | No           | No     | ✓\n",
      "    6 | No       | No           | Yes    | ✓\n",
      "    7 | Yes      | Yes          | No     | ✓\n",
      "    8 | Yes      | Yes          | Yes    | ✓\n",
      "    9 | Yes      | Yes          | Yes    | ✓\n",
      "   10 | Yes      | Yes          | Yes    | ✓\n",
      "   11 | Yes      | Yes          | Yes    | ✓\n",
      "   12 | Yes      | Yes          | Yes    | ✓\n",
      "   13 | No       | No           | No     | ✓\n",
      "\n",
      "Agreement: 14/14 predictions match (100.0%)\n",
      "\n",
      "ACCURACY COMPARISON:\n",
      "Our Implementation:  0.857 (85.7%)\n",
      "Scikit-Learn:        0.857 (85.7%)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TREE STATISTICS:\n",
      "Our Tree Depth: 5 (max allowed)\n",
      "Sklearn Tree Depth: 3\n",
      "Our Tree Nodes: 11\n",
      "Sklearn Tree Nodes: 11\n",
      "\n",
      "SCIKIT-LEARN FEATURE IMPORTANCES:\n",
      "Temperature: 0.552\n",
      "Humidity: 0.314\n",
      "Wind: 0.134\n",
      "\n",
      "Both implementations use Gini impurity and should produce similar results.\n",
      "Differences may arise from tie-breaking in split selection.\n"
     ]
    }
   ],
   "source": [
    "# Prepare data for scikit-learn (needs numerical encoding)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Encode features\n",
    "X_encoded = np.zeros((len(df_weather), 3))\n",
    "feature_encoders = {}\n",
    "\n",
    "for i, col in enumerate(['Temperature', 'Humidity', 'Wind']):\n",
    "    le = LabelEncoder()\n",
    "    X_encoded[:, i] = le.fit_transform(df_weather[col])\n",
    "    feature_encoders[col] = le\n",
    "\n",
    "# Encode target\n",
    "y_encoder = LabelEncoder()\n",
    "y_encoded = y_encoder.fit_transform(df_weather['Play_Tennis'])\n",
    "\n",
    "print(\"SCIKIT-LEARN VS OUR IMPLEMENTATION\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Train scikit-learn tree\n",
    "sklearn_tree = DecisionTreeClassifier(\n",
    "    criterion='gini',\n",
    "    max_depth=5,\n",
    "    min_samples_split=2,\n",
    "    min_samples_leaf=1,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "sklearn_tree.fit(X_encoded, y_encoded)\n",
    "\n",
    "# Compare predictions\n",
    "our_predictions = tree.predict(X_tennis)\n",
    "sklearn_predictions = y_encoder.inverse_transform(sklearn_tree.predict(X_encoded))\n",
    "\n",
    "print(\"PREDICTION COMPARISON:\")\n",
    "print(\"Index | Our Tree | Scikit-Learn | Actual | Match?\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "matches = 0\n",
    "for i in range(len(y_tennis)):\n",
    "    our_pred = our_predictions[i]\n",
    "    sklearn_pred = sklearn_predictions[i] \n",
    "    actual = y_tennis[i]\n",
    "    match = \"✓\" if our_pred == sklearn_pred else \"✗\"\n",
    "    if our_pred == sklearn_pred:\n",
    "        matches += 1\n",
    "    \n",
    "    print(f\"{i:5d} | {our_pred:8} | {sklearn_pred:12} | {actual:6} | {match}\")\n",
    "\n",
    "print(f\"\\nAgreement: {matches}/{len(y_tennis)} predictions match ({matches/len(y_tennis)*100:.1f}%)\")\n",
    "\n",
    "# Calculate accuracies\n",
    "our_accuracy = np.mean(our_predictions == y_tennis)\n",
    "sklearn_accuracy = np.mean(sklearn_predictions == y_tennis)\n",
    "\n",
    "print(f\"\\nACCURACY COMPARISON:\")\n",
    "print(f\"Our Implementation:  {our_accuracy:.3f} ({our_accuracy*100:.1f}%)\")\n",
    "print(f\"Scikit-Learn:        {sklearn_accuracy:.3f} ({sklearn_accuracy*100:.1f}%)\")\n",
    "\n",
    "# Visualize both trees side by side\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n",
    "\n",
    "# Our tree (already visualized above, so we'll show feature importance)\n",
    "feature_usage = {'Temperature': 0, 'Humidity': 0, 'Wind': 0}\n",
    "\n",
    "def count_usage(node):\n",
    "    if node and not node.is_leaf():\n",
    "        feature_name = tree.feature_names[node.feature]\n",
    "        feature_usage[feature_name] += 1\n",
    "        count_usage(node.left)\n",
    "        count_usage(node.right)\n",
    "\n",
    "count_usage(tree.root)\n",
    "\n",
    "ax1.bar(feature_usage.keys(), feature_usage.values(), color=['skyblue', 'lightgreen', 'lightcoral'])\n",
    "ax1.set_title('Our Tree: Feature Usage', fontsize=14, weight='bold')\n",
    "ax1.set_ylabel('Number of Splits')\n",
    "\n",
    "# Scikit-learn tree visualization\n",
    "plot_tree(sklearn_tree, \n",
    "          feature_names=['Temperature', 'Humidity', 'Wind'],\n",
    "          class_names=y_encoder.classes_,\n",
    "          filled=True,\n",
    "          ax=ax2)\n",
    "ax2.set_title('Scikit-Learn Tree Structure', fontsize=14, weight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Show tree statistics\n",
    "print(f\"\\nTREE STATISTICS:\")\n",
    "print(f\"Our Tree Depth: {tree.max_depth} (max allowed)\")\n",
    "print(f\"Sklearn Tree Depth: {sklearn_tree.get_depth()}\")\n",
    "\n",
    "def count_nodes(node):\n",
    "    if node is None:\n",
    "        return 0\n",
    "    return 1 + count_nodes(node.left) + count_nodes(node.right)\n",
    "\n",
    "our_node_count = count_nodes(tree.root)\n",
    "sklearn_node_count = sklearn_tree.tree_.node_count\n",
    "\n",
    "print(f\"Our Tree Nodes: {our_node_count}\")\n",
    "print(f\"Sklearn Tree Nodes: {sklearn_node_count}\")\n",
    "\n",
    "# Show feature importances from sklearn\n",
    "print(f\"\\nSCIKIT-LEARN FEATURE IMPORTANCES:\")\n",
    "for i, importance in enumerate(sklearn_tree.feature_importances_):\n",
    "    feature_name = ['Temperature', 'Humidity', 'Wind'][i]\n",
    "    print(f\"{feature_name}: {importance:.3f}\")\n",
    "\n",
    "print(f\"\\nBoth implementations use Gini impurity and should produce similar results.\")\n",
    "print(f\"Differences may arise from tie-breaking in split selection.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36d3cdb",
   "metadata": {},
   "source": [
    "## Advanced Topics & Real-World Application\n",
    "\n",
    "Let's explore more advanced concepts and apply our knowledge to a larger, more realistic dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43ccc011",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IRIS DATASET ANALYSIS\n",
      "==================================================\n",
      "Dataset shape: (150, 4)\n",
      "Features: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "Classes: ['setosa' 'versicolor' 'virginica']\n",
      "Samples per class: [50 50 50]\n",
      "\n",
      "Training set: 105 samples\n",
      "Test set: 45 samples\n",
      "\n",
      "📊 IRIS CLASSIFICATION RESULTS:\n",
      "Training Accuracy: 0.971 (97.1%)\n",
      "Test Accuracy: 0.889 (88.9%)\n",
      "\n",
      "DETAILED CLASSIFICATION REPORT:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        15\n",
      "  versicolor       0.78      0.93      0.85        15\n",
      "   virginica       0.92      0.73      0.81        15\n",
      "\n",
      "    accuracy                           0.89        45\n",
      "   macro avg       0.90      0.89      0.89        45\n",
      "weighted avg       0.90      0.89      0.89        45\n",
      "\n",
      "\n",
      "CONFUSION MATRIX:\n",
      "Predicted →\n",
      "Actual ↓    setosaversicolvirginic\n",
      "setosa          15       0       0\n",
      "versicol         0      14       1\n",
      "virginic         0       4      11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/20/thkdlvcn7c10s84s_9g7sf9m0000gn/T/ipykernel_73552/2241908827.py:152: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax5.scatter(X_2d[idx, 0], X_2d[idx, 1], c=color,\n"
     ]
    },
    {
     "data": {
      "image/png": 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f57bnzp3Tu+++G+fnF+nSpUvPFFskX19feXl5PfV277//vrZs2aIVK1bo/v375vKxY8eqdOnSzxUTAACArTxLH6xKlSr64YcfJElFixZVuXLlVLBgQfn4+Kh69eoqUKCAWbZSpUqyWCwyDENffvmlfv31V7366qsqXLiwypQpo+rVqyfqaCBJ5uiMhEqKGJ+1DxqpSpUqypMnj/m+VatWat++vUJDQyVJhw4d0vvvv/9cMT6PlPD3GgDg5RdXH2Dv3r3mz1u2bJGdnV2c2+/fv1/Nmze32iZdunTq169fjPJ58+Z9YkyJ1c+I/vsz6vVAJycntW7dWsOHD5ck/fPPP7p+/bqyZMkSo57Evh4YdabNO3fuaNWqVTp16pTOnDmjSpUqae/evcqZM2eMY3jW/sKzeu+995QmTRpJj69f5s2b1xydG/W4I0cRS1LdunWtZs157733NHTo0Fjrv3nzpnx9fc0+clziu67Zp0+fWEfsd+rUSaNGjTJHoP/2228qXbq0Vq5caZbp0KFDvPuNdOfOHav3CZlx6Pr163rrrbd08OBBSZKnp6c2bNggFxeXBO0zqqftp8fmzJkzVrM3RfX+++/HOatWgwYNVKxYsRjLo7a1evXqKXPmzOb7Nm3aqGPHjmafe9++fXr33Xfjjc+W3zcJcerUKd26dct836ZNG6sYfX199fXXX5sx+vv7q0WLFjHq6dixoxwcHqdAM2bMqMyZM+vq1auSYv8uid7WordFpByxt3ggFYua+P37778T/Msw6pdp1qxZY6yP2oGMqxOXO3duq/dRpz2JiIhIUByxiX4MkdMBRZczZ04tXLjQ/OX6559/atmyZRo1apSaNGkiLy8vLV++/In7i3580c9H9M50Qs5H9Clgnud8dO7c2ezEXb58WdLjjmOGDBni3a5x48YJ6vTGdX4TqlChQs+8bWSiP1LklPgAAABJ4Vn70vF5lj7Y3LlzzSkPb968qU2bNmn69On68MMPVbBgQbVq1crsT77++uuaOnWq0qZNK8MwdPjwYX377bf6+OOPVa9ePeXMmTPBU5EmxK1bt3Tjxg3zfUJuPE2KGJ+nDyrF7PPb29srU6ZM5vu4+vwJ/VvleSXnv9cAAKnH33//bf7s4uJi/q6MmgR6kshpz6Nu4+3tLXt7+2eKKbH6Gcn1eqC7u7v69eunfv36acyYMdq/f785PfKFCxespntOjP7Cs/ZtEtrPiJqse9I5jqpjx45PTIpL8ccbV38xc+bMVo+4+eqrr3Tp0iVzCnFnZ2dzKvgniT51dWBgYLzlT58+rQoVKmj//v2SHv9f2Llz5zP3baP+H5US1lePj4ODg7Jly6aGDRtqzZo1mj9/fpxl44o5vnaZ0D53VLb8vkmIF/FdIj257x69rTGNesrFiHEgmjfffFOnT5+W9PhL9fvvv0/QsxGjJlWjPmsjUuTdSNHLRuXo6Gj1Pq7n3yRE1Luooj5DUZJ5fLFp3bq1mjVrpoMHD+r48eM6ffq0duzYoSNHjujevXvq2LGjGjZsaN7BGJvoxxf9fEQ9F7GVjxT1fDzPuYjO09NTzZs3N5+RIsVMKEd36tQp/f777+b73r17a9CgQcqSJYssFouyZs1qdhSel5ub2zNt9+jRI3Xp0iXGsq5du8b5rBUAAIDE9Kx96bg8ax/M29tb/v7++ueff3Tw4EGdPn1ax44d0/r16xUWFqYVK1aoXr165nMOe/XqpQ8//FD79+/XH3/8odOnT2vz5s06ffq0bty4ofbt2+vcuXPPfBxRLViwwOqC6Ztvvpmg7V50jM/aB40Uvc8fHh6umzdvmu+jXliKHJUmWf+tEhERYT5DPLElt7/XAACpz6FDh6z6NdWqVTOv32XIkMHs01SvXl3169ePs57I5+JGHSl88eJFhYeHP3OyKjH6GbFdD4yasLP19cBI6dOnV8GCBfXrr79Ksh6N+6z9hWe9DhtVQvsZ6dOnN/tYT7rmGun+/fvauHGj+b5169aaNGmSvLy8ZGdnp9dff908H/GJr7/YvXt3ffPNN5KkJUuWKGfOnGZ/r3Hjxk8ckBQp+gxG169ft2rrUe3du1dvv/22eT5KliypjRs3PlcyO3riOqF99aiqVav2TDetxnV+o34/PKnPnZDzbOvvm4TEF1Vi5BakJ3+fRN/P88ymheSNEeNANN26dbPqzHTp0kXHjh2LUS4kJESzZs0yp6yuWLGiue7QoUM6efKk+X7Pnj3mNDvRy74oUS88nTp1Snfv3pUkXblyRYsWLYp1m1u3bun8+fNydHRUpUqV5OfnpylTppjTnkuPO1J//fVXvPt+/fXXrX4xLl682PzZMAyr92nSpLGajjCpdO/e3fy5WrVqT4whagdDejzCPGvWrLJYLNq+fXu8SfHIKVsk6cGDB88Y8ZP169dPf/zxh6THd8S5urpKkpYvX252TAEAAF6kZ+1Lx+VZ+2C///67IiIiVKBAAbVp00bDhw/X6tWrrS76/Pbbb5Kk//77T1evXpWbm5vefPNNde/eXTNmzLCaKen8+fMxYnkWGzdu1LBhw8z36dKl0wcffPDE7Z4lxqTqg0bas2eP1QXz5cuXm1M6So+nI40U9W+VAwcOmD8vXLgw1ovQkaJe3HraY0puf68BAFKXU6dOqXXr1lbL+vTpY/4c9XfPlStX1KVLF3OUc+Trww8/lLe3t0qWLCnp8RTokYKCgvTZZ5/F2O/58+efGFti9YWi//6Mev0vJCTEatBGgQIFYp1GPSncvXvXKlkd9XGQz9pfiNq3OXjwoJkQ3r59u9nnTCxR+1SbN2+2Gsn77bffxrrN3bt3rY6zRYsWypkzp+zs7HTy5EmrGzaeVenSpc0ZmwIDAzVmzBhzXUKnUZceJzmjTqF/5MiRWMutXLlSNWvWNNtl/fr1tWfPnmdOikdERGj48OFWNxBUqFDBPCZbitrWNm/ebDX71JIlS6z63AnpxybF9030pPTT9N0LFy5slYhfsmSJ1QjvqNe5LRZLon1Ghw8fNn/28fFhxHgKxohxIJqiRYtq5MiR+vjjjyU9/uVQunRpvfXWW+Yvgr///tv8JfTee+9Jkrp27aq5c+cqJCREERERqlatmnx9fRUWFmY+80JK+MWv5xW1kxQYGKjSpUurbNmy2rlzZ5wXEP/++29VqFBBZcuWVYkSJeTl5SUHBwdt3rzZqtyTfilkzpxZbdu21cKFCyVJS5cu1Z07d/T6669r165dVnfMde3aNca0SEmhfPny2rhxo0JDQ+Xj4/PE8gUKFJCdnZ35S/i9995T69atFRAQYB5nXHLkyGF2DKZMmaKbN2/K1dXVfG57Yvjhhx80e/ZsSY/vUl2yZIl+/fVX9erVS9LjGwEqV66s/PnzJ8r+AAAAYvOsfem4PGsfrFWrVrp7966qV6+uHDlyKGPGjPr333+1adMms0xkn3b37t169913VblyZRUpUkReXl4KDw/XmjVrzLJOTk7mTYdPY968efLw8NCNGze0e/duqySwxWLR/PnzrZ4RGJdniTEp+qBRhYaGqlKlSmrbtq2CgoKsRtukT5/e6rl/ZcqU0datWyVJixYt0pUrV+To6Kgff/wx3n1EvdD5ww8/aNCgQcqcObMyZ85sjv6PS3L7ew0AkLJF9nUCAwN15MgRbd68WWFhYeb6rl27qnbt2ub7vn37av369TIMQydPnlTRokXVtGlTZc6cWbdu3dLRo0e1Z88eZcuWzZyyun379ho7dqx5U1n//v21detWlS9fXvfu3dOePXv06quvPvG6VWL1hUqWLKk33njDvO43fvx4nT17VoUKFdLGjRutksy9e/dO0HlMDIGBgZo8ebKkxwni1atXW01HHjXh96z9hTJlypgJ3F27dqly5cry9PR8Yt/mWXTs2FFbtmwxj6dcuXJq1aqVLl26ZHUzQlRZs2ZV+vTpzePu2bOnOTvowoULFRISkiixde/e3ZzS/NGjR5IeP7qzVq1aT1VPjRo1dOrUKUnS/v37Y9xUsnLlSrVq1cq8ASFr1qyqWrWqPv/8c6ty3t7eVlO8RxXZLkJDQ3X58mX9+OOPVjMXZciQ4Yn/d5JKr1699P3330t6HPfrr7+u1q1b6/bt21bt0tvbW82aNXtifUnxfZMlSxY5OjqaSfuhQ4fq6NGjcnJy0htvvGGVu4jO3t5ePXr00IgRIyRJv/zyi6pWraqaNWvq6NGj5rmQpObNm8vb2zvhJzMekVP/S3ohfy8hGTEAxGrq1KmGk5OTISne1+3bt81tli5dajg7O8dZNk2aNMamTZus9lOtWjVzva+vr9W64cOHm+ty586d4HWGYRgPHjww8ufPHyMGi8Vi1KxZ02pZJH9//yceb9OmTc3yO3bssFp39uxZc11gYKBRqVKleOuqV6+eERwcnKD6DMOwWrdgwYJ4P79Ivr6+5jaZMmV6YvkFCxbEGYOfn1+sx1GjRg0jR44c5vvhw4db1dm7d+9Yt/voo4+e6tiixxbpypUrRpYsWczlAwYMMAzDMCIiIozq1auby8uVK2eEhoYm6LwBAAA8j6ftS589e9Zq+Y4dO8y6nqUPVrhw4Xj3mzFjRrOft3Tp0ifG2adPnwQdd9Q+enyvTJkyGevXr493+6h9/GeJ8UX3QQ3D+m+ZUqVKGenSpYuxPzs7O2PlypVW223evNmwWCwxyubOndvqs4v+99H3338f6zH5+PjEGlP07ZPy7zUAQOoS/ZpWXC8HBwdj9OjRRnh4eIw6ZsyYYdjb28e7ffTfN/v37zeyZs0aZ/mov7sSq58RX9/g8uXLxiuvvBJvXR07djQiIiISVF98fcT4RP39Hd/L29vbuHTpktW2z9JfOH78eKx93wwZMhhlypQx31erVs1qu/j6YvH1QVq0aBFrbNGPO2qdEyZMiHWbokWLGqVLl451X0+6VhtdSEiIkS1bNqtthgwZEu82sfntt9+sPqOo7cUwEt7njn6+E9ouSpQoYZw8efK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Gb6X+yCOPaOnSpUbHAAAAgIEaNWqkOpd/bRsAAAC4islk0vTp05WVlaXPPvtMX375pY4fP64pU6YYHS2fwMBAvfrqq1q3bp3Wr1+vli1b6o477tDOnTuLHZeQkKC6devajkaNGlVRYgCoGhkZWZKk1q0L77/cfvm8sjh4+LdSzb1/f8E+T8/855Ql1/6fDhU5NiPDNkPhE8uzhP689hPHjxTRD6A8DF0xLuVtNbFkyRJt3bpVYWFhuvbaa/P1z5o1y6BkAADAEbCxjGt46aWXFBUVpYULFyo4ONjoOAAAAHBQtWvX1i233GJ0jCK1bNlSLVu2tH3u1KmTDh8+rJkzZ6pLly5FjouJidGECRNsn0+dOkVxHIBTCQz0lSSlp+dtNX619PT855VFk0ZB+uWXzBLnbtasYN+5c1fOKWuuZtc3LnJsYKBtBuVtj17gyiX0513Y28evkD4A5WX4ivH09HTdfPPNqlOnjr7//nvt3r3bduzZs8foeAAAAKgCDz30kLZt26ZmzZrJy8tLPj4++Q4AAAC4npEjR+rw4cOlOvftt9/WW2+9ZedE5dOxY0f98MMPxZ7j4eGhOnXq5DsAwJlEdA5TcJMAxceblJubvy83V0pIMKlpcIAiOoeVee7kdxeqRg1p2jQVOnd8vFSjhrRiRcG+TZskDw+3cuUa9cRgeXi4FXrdzp2la66pIekFSVd1KlfSJrm5eUiKL6I/QQ18GykktOzPA0DRDF8xvm3bNqMjAAAAB2ZiybhLmDNnjtERAAAA4GB8fX3VunVrhYeHa8CAAQoLC1NQUJCuueYanThxQvv27dMnn3yi1atX67rrrtOrr75qdORC7d69W4FXlg4CgEsym82amRir+waPVWSkSTExVrVunbfaOiHBpORkac3KWJnN5jLPXbduXTVp3FjJyYc0cGDeO8Uvzx0fn/d+cUl66KGC1/3gA+npccP10pzXypzL3d1dY0YN18zZSwu9bt47xj+UFCkpRnnbo6dLSpD0gQbe+ze9+87SIvqT9djoueV6HgCKZnhh/M9++eUXmUwmXXfddUZHAQAAQBUaOnSo0REAAADgYP75z39qzJgxWrp0qRYuXKj0y/vZXuLl5aU777xTS5YsUc+ePe2S4cyZM/rxxx9tnw8cOKA9e/bIx8dHjRs3VkxMjH799Ve9/vrrkvJ+4TM4OFitWrVSTk6O3nzzTa1bt07r1q2zSz4AqE4GRfbUmpUva2L0NIWHZ9ramwb7a83KWA2KLP//lv/w9Va1aNVDmzYdshXCpbyV4s2ub6zp054p9rodO7QrV64Z8VGSpLkLlik5+crKbw8PN4X3Hqwut3bU4vmJOpYVbutr4NtIj42eq04RvdQypF2x/QAql+GF8dzcXL3wwgt66aWXdObMGUl5/6h9+umnFRsbKzc3w3d7BwAAKNHFixcVFxent956S5mZmQoMDNSwYcP0j3/8g3/PlNG5c+d04cKFfG1sJQkAAOCa/Pz8FBMTo5iYGP3vf//TwYMHde7cOTVo0EDNmjWz+w5TaWlp6t69u+3z5feADx06VMuXL1dGRoYOHTpk68/JydHEiRP166+/ytPTU61atdIHH3ygPn362DUnAFQXgyJ7amD/O5SyK00ZGVkKDPRVROewSlkZ/cPXW3Xy5En1u3ukDh7+TU0aBSn53YWqW7euJBV73YrkmhEfpRfixmnBopXa/9MhNbu+sR594hHNSTql9g191CH8Tu3bm6YTx4/I28dPIaFX5u0U0avYfgCVy/DCeGxsrJYuXarExER17txZVqtVu3btUlxcnM6fP69p06YZHREAABjIrZrspD59+nQtXLhQK1asUKtWrZSWlqbhw4erbt26euqpp4yO5/DOnj2rSZMm6Z133tGxY8cK9FssFgNSAQAAwJHUq1dP9erVq9JrduvWTVartcj+5cuX5/scFRWlqKgoO6cCgOrNbDarW5db7TJ33bp1lfLxqnJdtyK53N3dNW7MMNvn838qv5nNZoW2K/66xfUDqDyGF8ZXrFihJUuWaMCAAba2tm3b6rrrrtOoUaMojAMAgGohNTVVAwcOVN++fSVJwcHBWrVqldLS0gxOVj1ERUVp27ZtWrBggR555BHNnz9fv/76qxYtWqTExESj4wEAAAAAAACo5gzf1/P48eO68cYbC7TfeOONOn78uAGJAACAIzGZTIYe2dnZOnXqVL4jOzu7QM7bbrtNH330kb7//ntJ0pdffqlPPvmELRNL6f3339eCBQt07733qkaNGoqIiNA//vEPxcfH66233jI6HgAAAAAAAIBqzvDCeNu2bTVv3rwC7fPmzVPbtm0NSAQAAHBFQkKC6tatm+9ISEgocN6kSZP04IMP6sYbb1TNmjXVvn17jRs3Tg8++KABqauf48ePq2nTppLy3id++Rckb7vtNu3cudPIaAAAAAAAAACcgOFbqc+YMUN9+/bV//3f/6lTp04ymUz69NNPdfjwYX344YdGxwMAAC4uJiZGEyZMyNfm4eFR4Ly3335bb775plauXKlWrVppz549GjdunIKCgjR06NCqilttXX/99fr555/VpEkThYSE6J133lGHDh30/vvvV/l7JAEAAAAAgGSxWJSyK00ZGVkKDPRVROcwmc1mQzPl5ORowaKV2v/TITW7vrFGPTFY7u7upRpb0v0UN7cjPgt7slgs2rc3TSeOH5G3j59CQp37fuE6SlUY37BhQ6kn/PO7wkuja9eu+v777zV//nx9++23slqtGjRokEaNGqWgoKAyzQUAAJyPyeDre3h4FFoIv9ozzzyj6OhoPfDAA5Kk0NBQHTx4UAkJCRTGS2H48OH68ssv1bVrV8XExKhv376aO3euLl68qFmzZhkdDwAAAAAAl7I+aYsmRk/TzwczbW3BTQI0MzFWgyJ7GpIpavIMzV2wTNnZuba2mGena8yo4ZoRH1Xs2JLup7i5O3Zo53DPwp5SUzZr8fxEHcv6xdZW37ehRoyOVqeIXgYmAyquVIXxyMjIUk1mMplksVjKHCIoKEjTpk0r8zgAAABH8ccff8jNLf9basxms3Jzc4sYgT8bP3687fvu3bvrm2++0eeff65mzZrxeh0AAADo999/18SJE/XRRx/pyJEjslqt+frL8zNJAEDh1idt0X2Dx6pfP6tWrZZat5bS06X4+N913+CxWrPy5SovCEdNnqGZs5eqXz8pNvZKpmnTcjVz9lJJKrI4XtL99O97uzYkf1Tk3CaT1K+fHOZZ2FNqymYlxo2R1E/SGkmtJaXrWFa8EuPGKDpuLsVxVGulKoxX9g90v/rqq1Kf26ZNm0q9NgAAqF7cTEavGS+d/v37a9q0aWrcuLFatWql3bt3a9asWXr00UeNjlYtNWnSRE2aNDE6BgAAABzEsGHDdOjQIT377LMKDAyUqZr8dwIAVDcWi0UTo6epXz+rkpKky2sAOnaUkpKsiow06ZmYaRrY/44q21o7JydHcxcsU79+0nvv5c/03nvSwIHSvAXL9ELcuALbqpfmfjZu+kh9+xace/16ycdH6tZNDvMs7MlisWjx/ETlFcWTJF1eANLx0udILZk/XR3C73SK+4VrMuQd4+3atZPJZCrwm51XK+8KdAAAgKo2d+5cPfvssxo1apSOHDmioKAgPfHEE5oyZYrR0Rzav//9bx0/fly9e/e2tb3++ut67rnndPbsWUVGRmru3Lml2s4eAAAAzuuTTz5RSkqK2rVrZ3QUAHBqKbvS9PPBTK1afaUQfJmbmxQTY1V4eKZSdqWpW5dbqyTTgkUrlZ2dq9jYwjNNniwlJ+dqwaKVGjdmWL7+0tzP++9LffoU7N+1SzpzRkVe14hnYU/79qZd2j59ja4UxS9zkxSjo1nh2rc3TaHtqv/9wjWVqzB+9uxZ7dixQ4cOHVJOTk6+vrFjx5Y4/sCBA+W5LAAAgMPy8vLSnDlzNGfOHKOjVCtxcXHq1q2brTC+d+9e/e1vf9OwYcN000036cUXX1RQUJDi4uKMDQoAAABDNWrUqMRFNgCAisvIyJKUt2V4YS63Xz6vKuz/6VC+a1/tcvvl8/6stPfj6VmwLyMj/zlFja3KZ2FPJ44fufRdETd8qf3KeUD1U+bC+O7du9WnTx/98ccfOnv2rHx8fHT06FHVqlVLfn5+pSqMsy0mAAAoLXZIdG579uzRP//5T9vn1atX69Zbb9XixYsl5f0A9LnnnqMwDgAA4OLmzJmj6OhoLVq0SMHBwUbHAQCnFRjoKynvPdodOxbsT0/Pf15VaHZ941Jlunzen5X2fs6dK9gXGHjlHEd5Fvbk7eN36bt05W2ffrX0q84Dqp+r90Io0fjx49W/f38dP35cnp6e+uyzz3Tw4EH95S9/0cyZM8sVYv/+/RozZozuvPNO9ejRQ2PHjtX+/fvLNRcAAACqjxMnTsjf39/2eceOHbrrrrtsn2+55RYdPnzYiGgAAAAwmLe3t3x8fOTj46MHHnhA27dvV7NmzeTl5WVrv3wAACpHROcwBTcJUHy8Sbm5+ftyc6WEBJOaBgcoonNYlWUa9cRgeXi4ado0FZopPl66xsNNo54YXGBsae6nZk3pww8Lzt25s1S7tvTCC4Vf14hnYU8hoWGq79tQUrykq25YuZIS1MC3kUJCneN+4ZrKvGJ8z549WrRokcxms8xms7Kzs3X99ddrxowZGjp0qAYNGlSm+TZv3qwBAwaoXbt26ty5s6xWqz799FO1atVK77//vnr06FHWiAAAwImYWDLu1Pz9/XXgwAE1atRIOTk5+uKLLzR16lRb/+nTp1WzZk0DEwIAAMAovKYIAKqe2WzWzMRY3Td4rCIjTYqJsap167zV0QkJJiUnS2tWxspsNldZJnd3d40ZNVwzZy/VwIF57xS/nCk+XkpOliaOHy53d/dy3U//vndoQ/JHhc595kxe0dxRnoU9mc1mjRgdrcS4MZIiJcUob/v0dEkJkpL12Oi5TnO/cE1lLozXrFnT9gNqf39/HTp0SDfddJPq1q2rQ4cKvr+hJNHR0Ro/frwSExMLtE+aNInCOAAAgBO76667FB0drenTpyspKUm1atVSRESErf+rr75Ss2bNDEwIAAAAowwdOtToCADgkgZF9tSalS9rYvQ0hYdn2tqbBvtrzcpYDYrsWeWZZsRHSZLmLlim5OQrq5mv8XDTxPHDbf2FKc39RE2eUeTcHTu0c6hnYU+dInopOm6uFs9P1LGscFt7A99Gemz0XHWK6GVgOqDiylwYb9++vdLS0nTDDTeoe/fumjJlio4ePao33nhDoaGhZQ7wzTff6J133inQ/uijj/JboQAAAE7uhRde0KBBg9S1a1fVrl1bK1asyPcb3q+99pp69nSu/8gEAABA2ZnNZmVkZMjPL/97TY8dOyY/Pz9ZLBaDkgGAcxoU2VMD+9+hlF1pysjIUmCgryI6hxm6WnhGfJReiBunBYtWav9Ph9Ts+sYa9cTgQleKX62k+ylpbkd7FvbUKaKXOoTfqX1703Ti+BF5+/gpJNR57xeupcyF8fj4eJ0+fVqS9M9//lNDhw7V3//+dzVv3lzLli0rcwBfX1/t2bNHLVq0yNe+Z8+eAv/QBQAAroed1J2br6+vUlJSdPLkSdWuXbvAf2StWbNGtWvXNigdAAAAHIXVai20PTs7u1QFEQBA2ZnNZnXrcqvRMfJxd3fXuDHDyjW2pPspbm5HfBb2ZDabFdrOde4XrqPMhfGwsDDb976+vvrwww8rFGDEiBF6/PHH9dNPPyk8PFwmk0mffPKJpk+frqeffrpCcwMAAKB6qFu3bqHtPj4+VZwEAAAAjuTll1+WJJlMJi1ZsiTfL01aLBbt3LlTN954o1HxAAAAUI2UuTBe2Z599ll5eXnppZdeUkxMjCQpKChIcXFxGjt2rMHpAACA0dxYMg4AAAC4rNmzZ0vKWzG+cOHCfDsMubu7Kzg4WAsXLjQqHgAAAKqRMhfGmzZtKlMxP6D+6aefyjSfyWTS+PHjNX78eNsW7V5eXmWNBQAAAAAAAMDJHDhwQJLUvXt3rV+/Xt7e3gYnAgDHYrFYDHn39blz5/RMzAz98ONBtWjeRC8mRMnT09PWn5OTU+T7uovrK6m/pPstrt+oZwXAcZS5MD5u3Lh8ny9cuKDdu3dr06ZNeuaZZ8oc4MCBA7p48aJatGiRryD+ww8/qGbNmgoODi7znCi7t1e9peXLlupoVpaaNW+hqOjJuvkvYSUPBEqhc2gjjf9rR93cIkCBDbx0/5S1en/X94WeO3d8bz3Wr72emb9V89b/t4qTwpnt+SJNK994Td99s0/HjmYpfubL6tLtDqNjAQAAAABKYdu2bUZHAACHsz5piyZGT9PPBzNtbcFNAjQzMVaDInva7bqR943Sxs0f6cKFvM9bP9qlxa+tVO9edyhpzQJFTZ6huQuWKTs71zYm5tnpGjNquCQV2TcjPqrYsR07tCv2fot7HpIMeVYAHEuZC+NPPfVUoe3z589XWlpamQMMGzZMjz76qFq0aJGv/d///reWLFmi7du3l3lOlM2mjR9qRmKCYp99Tu3a36y176zWqCdG6N0NHygwKMjoeHAC13rW1N79R/TGpq+0euo9RZ7Xv/MNuuXGIP129HQVpoOrOHfunJq3aKm+/e9WbNQ4o+OgDNhJ3XndfPPN+uijj+Tt7a3nn39eEydOVK1atYyOBQAAAAcxYcKEUp87a9YsOyYBAMezPmmL7hs8Vv36WbVqtdS6tZSeLsXH/677Bo/VmpUv26XgG3nfKG1I/kj9+kmxsVeuO22atCH5I7Vo1UP7fzpUSH+uZs5eKklF9m3f+R+lfb632LH9+6vQ+3163KN6ac5rhT6Pex8cI5Mp77pV+awAOB6T1Wq1VsZEP/30k9q1a6dTp06VaVydOnX0xRdfqHnz5vnaf/zxR4WFhel///tfmbOcv1jmIS5tyAP36aaQEP1jylRbW2T/3up++516avzTBiarXrx7xRsdoVo499HkQleMBzWorZ3zhqn/pNV6N/5+zVv3X1aMl+BQUpTREaqt28JasWK8HHy9yvz7dJVi1Pp9hlz3sgWDQgy9vjPz9PTUDz/8oIYNG8psNisjI0N+fn5GxyqVU6dOqW7dujp58qTq1Klj12ut/y7DrvMDkjSoZaDREQr1rxP/MjoCnNxT3oX/8r8jODl1asknARVQ97nnquQ6Ffl3U/fu3fN9/vzzz2WxWNSyZUtJ0vfffy+z2ay//OUv+vjjjystsyO4/Ny+X/yavPjlUTgR82/75H3ndTLf2M3oKNWaxWJRi1a3K7RNppKSJDe3K325uVJkpEnpe/31ffrHlbpV+Llz51TPv5169ZLee6/gdQcMkDZvlu66q+j+nTulY8ekmjUrb2xkpElbtpjUo0dugbEXLkg+PlK3boXPa69ndV41NOOdo2rf0KfS5gRQuD/OntaDA24u1b83K+0n3GvXrpWPT9n/D9xkMtneLf5nJ0+elMViqYxoKMaFnBx9s+9rPfrY4/naO4V31pd7dhuUCq7GZJKWRg/Q7Hf+rW8OHjU6DgAHY2LJuNNq166dhg8frttuu01Wq1UzZ85U7dq1Cz13ypQpVZwOAAAARvvz9umzZs2Sl5eXVqxYYXvP+IkTJzR8+HBFREQYFREADJGyK00/H8zUqtX5C71S3ueYGKvCwzOVsitN3brcWmnXfSZmhi5cyFvNXdh1e/eWPvig6P7Y2Lz+XbvyCtWVNTYmxqr337eqd++CY3ftks6cKXpeez0rAI6pzIXx9u3b5/sBtdVqVWZmprKysrRgwYIyB4iIiFBCQoJWrVpl+20ci8WihIQE3XbbbWWeD2Vz4n8nZLFYVL9+/Xzt9es30NGjWQalgqt5+oFOumjJ1XxWiAOAS1m+fLmee+45JScny2QyaePGjapRo+A/T00mE4VxAAAAF/fSSy9py5YttqK4JHl7e+uFF15Qz5499fTT7HoIwHVkZOT97L5168L7L7dfPq+y/PDjwWKv6+mpYvuv5KrcsZf7Ls/xZ5fPr+pnBcAxlbkwPnDgwHyFcTc3N/n6+qpbt2668cYbyxxgxowZ6tKli1q2bGn77c6UlBSdOnWqVFsgZWdnKzs7O1+b1ewhDw+PMmdxZVevxrNarazQQ5Vo3yJAowfdovCRrxkdBQBQxVq2bKnVq1dLyvs35UcffVRttlIHAABA1Tp16pR+//13tWrVKl/7kSNHCt2NEgCcWWCgr6S892R37FiwPz09/3mVpUXzJtr60a4ir3vu3JXrF5+rcsde7rs8x59dPr+qnxUAx1TmwnhcXFylBggJCdFXX32lefPm6csvv5Snp6ceeeQRPfnkk6Xamj0hIUFTr3rvVuyzz+kfUyo3p7Pyructs9mso0fzb199/Pgx1a/fwKBUcCWdQxvJr961+n7Vk7a2GmY3JY68Q0/ec4tuHFL2nSgAOBe3kk+BE8jNzTU6AgAAABzY3XffreHDh+ull15Sx0uVjc8++0zPPPOMBg0aZHA6AKhaEZ3DFNwkQPHxvyspyVrgvdkJCSY1DfZXROewSr3uiwlRWvzaSk2bVvj7ujdulGrUUJH906ZJXl5S58755y3t2Nq1Cx+bkGCSh4dJGzfmauTI/GM7d84b98IL0oYNBee117MC4JjK/LNms9msI0eOFGg/duyYbSv0sgoKClJ8fLw++OADrV27VlOmTCn1+8pjYmJ08uTJfMczk2LKlcMV1XR3100hrfTZp7vytX/26adq2669QangSlb+X7puGbFEtz6+1Hb8dvS0Zr/zmfpPWm10PABAFdq/f7/GjBmjO++8Uz169NDYsWO1f/9+o2MBAADAASxcuFB9+/bVQw89pCZNmqhJkyYaMmSIevfuXa7XOwJAdWY2mzUzMVbJyVJkpEmpqdLp01Jqat7n5GTpxYTYctdsiuLp6aneve5QcrI0cKDyXXfgwLx3gDdp3LjY/tOnpUGDCu9r1za02LFnzkj33FP4/Y4ZNVwffGAq8DzuucekM2ekDz+s2mcFwDGVecW41WottD07O1vu7u6lmuOrr75S69at5ebmpq+++qrYc9u0aVNsv4dHwW3Tz18sVQxc8vDQ4YqNjlJI69Zq27a91q15WxkZGbrvrw8YHQ1O4tpraqrZdVfeARYcUFdtmvnpxOnzOnzklI6fyr/HzYWLFv1+/Kx++OV4VUeFE/vjj7P69fAh2+eMX3/RD999I6+6dRUQEGRgMpSEV3u4hs2bN2vAgAFq166dOnfuLKvVqk8//VStWrXS+++/rx49ehgdEQAAAAaqVauWFixYoBdffFH79++X1WpV8+bNde211xodDQAMMSiyp9asfFkTo6cpPDzT1t402F9rVsZqUGRPu1w3ac0CRd43Shs3f6Tk5CvtNWtKA/rdoaQ1CxQ1eYbmLlim5OQru8Nd4+GmieOHS1KRfTPio4od27FDu2Lvt6j+tatiJanKnxUAx1PqwvjLL78sKe+H00uWLFHt2rVtfRaLRTt37iz1O8bbtWunzMxM+fn5qV27djKZTIUW3E0mkywWS2kjopzu6t1HJ/93Qq++skBZWUfUvMUNmr/wVQUFXWd0NDiJm1sGasush2yfZ4zKK268sfkrPT4juahhQKX6dt/XGjtyuO3z3NkzJEm9+w1UbFy8UbEAXBIdHa3x48crMTGxQPukSZMojAMAAECSdO2115a4kAYAXMWgyJ4a2P8OpexKU0ZGlgIDfRXROczuq5+T1izQuXPn9EzMDP3w40G1aN5ELyZEydPTU5I0Iz5KL8SN04JFK7X/p0Nqdn1jjXpisG1xZXF9JY0t7n5Leh5GPCsAjsVkLWoJ+FWaNm0qSTp48KAaNmyY738s3N3dFRwcrOeff1633npriXMdPHhQjRs3lslk0sGDB4s9t0mTJqWJlw8rxmEE714U1lC1DiVFGR0BLsbXq8wbzVSKsUnfGnLdy16OLN0v/qFirrnmGu3du1ctWrTI1/7999+rTZs2On/+vEHJCnfq1CnVrVtXJ0+eVJ06dex6rfXfZdh1fkCSBrUMNDpCof514l9GR4CTe8r7KaMjFOnk1KlGR4CTq/vcc1VynYr8u2nQoEFavny56tSpU+J7xNevX1+RmA7n8nP7fvFr8qpVy+g4QKUx/7ZP3ndeJ/ON3YyOAtjVedXQjHeOqn3D0r02GED5/XH2tB4ccHOp/r1Z6p9wHzhwQJLUvXt3rV+/Xt7e3iWMKNqfi93lKXwDAADX4cZO6i7B19dXe/bsKVAY37Nnj/z8/AxKBQAAACPVrVvX9mqlunXrGpwGAAAA1V2Zl35t27atUgOsWLFCDRo0UN++fSVJUVFRevXVVxUSEqJVq1ZROAcAAHABI0aM0OOPP66ffvpJ4eHhMplM+uSTTzR9+nQ9/fTTRscDAACAAZYtW1bo9wAAAEB5uJV1wL333lvg3Y+S9OKLL+q+++4rc4D4+HjbeydSU1M1b948zZgxQw0aNND48ePLPB8AAHAubiZjD1SNZ599VlOmTNHcuXPVtWtXdenSRfPmzVNcXJxiY2ONjgcAAACDLV68WD/88IPRMQAAAFCNlXnF+I4dO/RcIe8guuuuuzRz5swyBzh8+LCaN28uSUpKStK9996rxx9/XJ07d1a3bt3KPB8AAACqH5PJpPHjx2v8+PE6ffq0JMnLy8vgVAAAAHAUL730kp544gkFBASoa9eu6tatm7p27aobb7zR6GgA4LQsFotSdqUpIyNLgYG+iugcJrPZXGJfRea151gj5wbgGMpcGD9z5ozc3d0LtNesWVOnTp0qc4DatWvr2LFjaty4sbZs2WJbJX7NNdfo3LlzZZ4PAAAA1RsFcQAAAFzt22+/VWZmprZt26YdO3Zo9uzZGjVqlHx9fdWtWzetXr3a6IgA4FTWJ23RxOhp+vlgpq0tuEmAZibm7epWVN+gyJ7lnteeY0tiz7kBOI4yb6XeunVrvf322wXaV69erZCQkDIH6NGjhx577DE99thj+v77723vGv/6668VHBxc5vkAAIBzMZlMhh4AAAAAHENAQIAefPBBvfTSS/rXv/6lRx55RMeOHdPatWuNjgYATmV90hbdN3isQttkKjVVOn1aSk2VQtv8rnsfHKP7Bo8ptO++wWO1PmlLuea159iK3G9F5wbgWMq8YvzZZ5/VPffco/379+v222+XJH300UdauXJluf4ROn/+fD377LM6dOiQ1q1bp/r160uSPv/8cz344INlng8AAAAAAACAc9m4caN27Nih7du368svv1SrVq3UpUsXrVu3ThEREUbHAwCnYbFYNDF6mvr1syopSXK7tLyyY0dp3TqrfHykbt1UoC8pyarISJOeiZmmgf3vKLAFeXHz2nNsRe63onMDcDxlLowPGDBASUlJio+P19q1a+Xp6am2bdvq448/Vp06dco018WLF/Wvf/1LUVFRatSoUb6+qVOnljUaAABwQm4s2gYAAABcXt++feXr66unn35amzdvVt26dY2OBABOKWVXmn4+mKlVq68UiS/btUs6c0aKjS3Y5+YmxcRYFR6eqZRdaerW5dZSz2vPsRW534rODcDxlHkrdSnvH6K7du3S2bNn9eOPP2rQoEEaN26c/vKXv5Rpnho1aujFF1+UxWIpTwwAAAA4gQsXLqh79+76/vvvjY4CAAAABzVr1ix17txZL774olq2bKm//vWveuWVV/TNN98YHQ0AnEpGRpYkqXXrwvpUZN+f2y/PUdp57Tm2JPacG4DjKVdhXJI+/vhjPfTQQwoKCtK8efPUp08fpaWllXmeO++8U9u3by9vDAAAAFRzNWvWVHp6Ou90BwAAQJHGjRun9evXKysrS1u3blVERIT+7//+T23btlVgYKDR8QDAaQQG+kqS0tML61ORfX9uvzxHaee159iS2HNuAI6nTFup//LLL1q+fLlee+01nT17Vvfff78uXLigdevWKSQkpFwBevfurZiYGKWnp+svf/mLrr322nz9AwYMKNe8AADAOVArdQ2PPPKIli5dqsTERKOjAAAAwIHt3r1b27dv17Zt25SSkqLc3Fw1bNjQ6FgA4DQiOocpuEmA4uN/V1KSNd/24p07S7VrSy+8IG3YkH/r8dxcKSHBpKbB/oroHFamee05tiL3W9G5ATieUhfG+/Tpo08++UT9+vXT3Llzddddd8lsNmvhwoUVCvD3v/9dUt52SFczmUxssw4AAOACcnJytGTJEm3dulVhYWEFflmysH8rAgAAwHUMGDBAn3zyiU6dOqV27dqpW7duevzxx9WlSxfVqVPH6HgA4DTMZrNmJsbqvsFjFRlpUkyMVa1b562cTkgw6cwZqz78UIX2JSdLa1bGymw2l3lee42t6P1WZG4AjqfUhfEtW7Zo7Nix+vvf/64WLVpUWoDc3NxKmwsAADgfN5aMu4T09HTdfPPNklTgXeNssQ4AAIAbbriBQjgAVJFBkT21ZuXLmhg9TeHhmbb2psH+WrsqVpIK7VuzMlaDInuWa157ji2JPecG4FhKXRhPSUnRa6+9prCwMN144416+OGH9de//rVSw5w/f17XXHNNpc4JAAAAx7dt2zajIwAAAMCBzZw50+gIAOBSBkX21MD+dyhlV5oyMrIUGOiriM5htpXTxfVVZF57jTVybgCOo9SF8U6dOqlTp07617/+pdWrV+u1117ThAkTlJubq61bt6pRo0by8vIqcwCLxaL4+HgtXLhQv//+u77//ntdf/31evbZZxUcHKy//e1vZZ4TAAAA1dOPP/6o/fv3q0uXLvL09JTVamXFOAAAAAAABjCbzerW5dYy91VkXnuONXJuAI7BrawDatWqpUcffVSffPKJ9u7dq6efflqJiYny8/PTgAEDyhxg2rRpWr58uWbMmCF3d3dbe2hoqJYsWVLm+QAAgHNxM/hA1Th27JjuuOMO3XDDDerTp48yMjIkSY899piefvppg9MBAAAAAAAAqO4q9PPeli1basaMGfrll1+0atWqcs3x+uuv69VXX9WQIUPybUnRpk0bffvttxWJBwAAgGpi/Pjxqlmzpg4dOqRatWrZ2v/6179q06ZNBiYDAAAAAAAA4AxKvZV6ccxmsyIjIxUZGVnmsb/++quaN29eoD03N1cXLlyohHQAAKA6Yxdt17BlyxZt3rxZDRs2zNfeokULHTx40KBUAAAAAABXYrFYXOod0yXdr6s9DwDOr1IK4xXRqlUrpaSkqEmTJvna16xZo/bt2xuUCgAAAFXp7Nmz+VaKX3b06FF5eHgYkAgAAABGO3XqVKnPrVOnjh2TAHAF65O2aGL0NP18MNPWFtwkQDMTYzUosqeByeyjpPt1tecBwDUY/urM5557Tk8++aSmT5+u3NxcrV+/XiNGjFB8fLymTJlidDwAAGAwN5PJ0ANVo0uXLnr99ddtn00mk3Jzc/Xiiy+qe/fuBiYDAACAUerVqydvb+9ij8vnAEBFrE/aovsGj1Vom0ylpkqnT0upqVJom9913+CxWp+0xeiIlaqk+42aPMOlngcA12H4ivH+/fvr7bffVnx8vEwmk6ZMmaKbb75Z77//vnr06GF0PAAAAFSBF198Ud26dVNaWppycnIUFRWlr7/+WsePH9euXbuMjgcAAAADbNu2zegIAFyAxWLRxOhp6tfPqqQkye3ScsKOHaWkJKsiI016JmaaBva/wym2ES/N/c5dsEx9+7rG8wDgWgwvjA8fPlwPPfSQtm/fLhOrsgAAAFxSSEiIvvrqK73yyisym806e/asBg0apNGjRyswMNDoeAAAADBA165djY4AwAWk7ErTzwcztWr1lSLwZW5uUkyMVeHhmUrZlaZuXW41JmQlKs39vv++Vb17u8bzAOBaDC+MHzt2TH379lX9+vX14IMP6qGHHlK7du2MjgUAABwEvzfnOgICAjR16lSjYwAAAMCB/fHHHzp06JBycnLytbdp08agRACqu4yMLElS69aF919uv3xedVfa+/X0LL7fWZ4HANdieGF8w4YN+t///qd33nlHK1eu1OzZs9WyZUs99NBDGjx4sIKDg42OCAAAgCpw4sQJLV26VN98841MJpNuuukmDR8+XD4+PkZHAwAAgMGysrI0fPhwbdy4sdB+i8VSxYkAOIvAQF9JUnp63nbhV0tPz39edVfa+z13rvDxzvY8ALgWt5JPsb969erp8ccf1/bt23Xw4EENHz5cb7zxhpo3b250NAAAYDA3k7EHqsaOHTvUtGlTvfzyyzpx4oSOHz+ul19+WU2bNtWOHTuMjgcAAACDjRs3TidOnNBnn30mT09Pbdq0SStWrFCLFi20YcMGo+MBqMYiOocpuEmA4uNNys3N35ebKyUkmNQ0OEARncOMCVjJSnO/Hh5u2rhRLvE8ALgWhyiMX3bhwgWlpaXp3//+t37++Wf5+/sbHQkAAABVYPTo0br//vt14MABrV+/XuvXr9dPP/2kBx54QKNHjzY6HgAAAAz28ccfa/bs2brlllvk5uamJk2a6KGHHtKMGTOUkJBgdDwA1ZjZbNbMxFglJ0uRkSalpkqnT0upqXmfk5OlFxNiZTabjY5aKUpzv2NGDdcHH5hc4nkAcC0OURjftm2bRowYIX9/fw0dOlReXl56//33dfjwYaOjAQAAoArs379fTz/9dL7/sDabzZowYYL2799vYDIAAAA4grNnz8rPz0+S5OPjo6ysvHfbhoaG6osvvjAyGgAnMCiyp9asfFl7v/JXeLhUp44UHi6l7/XXmpUva1BkT6MjVqqS7ndGfJRLPQ8ArsPwd4w3bNhQx44dU69evbRo0SL1799f11xzjdGxAACAg3AzsZ+5K7j55pv1zTffqGXLlvnav/nmG7Vr186YUAAAAHAYLVu21Hfffafg4GC1a9dOixYtUnBwsBYuXKjAwECj4wFwAoMie2pg/zuUsitNGRlZCgz0VUTnMKddGV3S/bra8wDgGgw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      "text/plain": [
       "<Figure size 2000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TREE ANALYSIS:\n",
      "Tree depth: 4\n",
      "Number of leaves: 6\n",
      "Total nodes: 11\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "INSIGHTS:\n",
      "• Optimal tree depth: 3\n",
      "• Best test accuracy: 0.978\n",
      "• Most important feature: petal length (cm)\n",
      "• Decision trees can achieve 97.8% accuracy on Iris!\n",
      "\n",
      "KEY TAKEAWAYS:\n",
      "• Gini impurity effectively measures node purity\n",
      "• Information gain guides optimal splitting decisions\n",
      "• Tree depth controls model complexity and overfitting\n",
      "• Feature importance reveals which attributes matter most\n",
      "• Decision boundaries can be visualized in 2D projections\n"
     ]
    }
   ],
   "source": [
    "# Load the Iris dataset for a more complex example\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "print(\"IRIS DATASET ANALYSIS\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "# Load data\n",
    "iris = load_iris()\n",
    "X_iris = iris.data\n",
    "y_iris = iris.target\n",
    "feature_names_iris = iris.feature_names\n",
    "class_names_iris = iris.target_names\n",
    "\n",
    "print(f\"Dataset shape: {X_iris.shape}\")\n",
    "print(f\"Features: {feature_names_iris}\")\n",
    "print(f\"Classes: {class_names_iris}\")\n",
    "print(f\"Samples per class: {np.bincount(y_iris)}\")\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_iris, y_iris, test_size=0.3, random_state=42, stratify=y_iris\n",
    ")\n",
    "\n",
    "print(f\"\\nTraining set: {X_train.shape[0]} samples\")\n",
    "print(f\"Test set: {X_test.shape[0]} samples\")\n",
    "\n",
    "# Train decision tree on Iris\n",
    "iris_tree = DecisionTreeClassifier(\n",
    "    criterion='gini',\n",
    "    max_depth=4,\n",
    "    min_samples_split=5,\n",
    "    min_samples_leaf=2,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "iris_tree.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_train = iris_tree.predict(X_train)\n",
    "y_pred_test = iris_tree.predict(X_test)\n",
    "\n",
    "# Calculate accuracies\n",
    "train_accuracy = accuracy_score(y_train, y_pred_train)\n",
    "test_accuracy = accuracy_score(y_test, y_pred_test)\n",
    "\n",
    "print(f\"\\n📊 IRIS CLASSIFICATION RESULTS:\")\n",
    "print(f\"Training Accuracy: {train_accuracy:.3f} ({train_accuracy*100:.1f}%)\")\n",
    "print(f\"Test Accuracy: {test_accuracy:.3f} ({test_accuracy*100:.1f}%)\")\n",
    "\n",
    "# Detailed classification report\n",
    "print(f\"\\nDETAILED CLASSIFICATION REPORT:\")\n",
    "print(classification_report(y_test, y_pred_test, target_names=class_names_iris))\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_test)\n",
    "print(f\"\\nCONFUSION MATRIX:\")\n",
    "print(\"Predicted →\")\n",
    "print(\"Actual ↓  \", end=\"\")\n",
    "for name in class_names_iris:\n",
    "    print(f\"{name[:8]:>8}\", end=\"\")\n",
    "print()\n",
    "\n",
    "for i, name in enumerate(class_names_iris):\n",
    "    print(f\"{name[:8]:8}  \", end=\"\")\n",
    "    for j in range(len(class_names_iris)):\n",
    "        print(f\"{cm[i,j]:8}\", end=\"\")\n",
    "    print()\n",
    "\n",
    "# Visualize the decision tree and results\n",
    "fig = plt.figure(figsize=(20, 15))\n",
    "\n",
    "# Tree structure\n",
    "ax1 = plt.subplot(2, 3, (1, 2))\n",
    "plot_tree(iris_tree, \n",
    "          feature_names=feature_names_iris,\n",
    "          class_names=class_names_iris,\n",
    "          filled=True,\n",
    "          fontsize=10)\n",
    "ax1.set_title('Decision Tree for Iris Classification', fontsize=16, weight='bold')\n",
    "\n",
    "# Feature importance\n",
    "ax2 = plt.subplot(2, 3, 3)\n",
    "importances = iris_tree.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "bars = ax2.bar(range(len(importances)), importances[indices], \n",
    "               color=['skyblue', 'lightgreen', 'lightcoral', 'plum'])\n",
    "ax2.set_title('Feature Importance', fontsize=14, weight='bold')\n",
    "ax2.set_xlabel('Features')\n",
    "ax2.set_ylabel('Importance')\n",
    "ax2.set_xticks(range(len(importances)))\n",
    "ax2.set_xticklabels([feature_names_iris[i][:10] for i in indices], rotation=45)\n",
    "\n",
    "# Add value labels\n",
    "for i, (bar, imp) in enumerate(zip(bars, importances[indices])):\n",
    "    ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "             f'{imp:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "# Confusion matrix heatmap\n",
    "ax3 = plt.subplot(2, 3, 4)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=class_names_iris, yticklabels=class_names_iris, ax=ax3)\n",
    "ax3.set_title('Confusion Matrix', fontsize=14, weight='bold')\n",
    "ax3.set_xlabel('Predicted')\n",
    "ax3.set_ylabel('Actual')\n",
    "\n",
    "# Class distribution\n",
    "ax4 = plt.subplot(2, 3, 5)\n",
    "class_counts = np.bincount(y_iris)\n",
    "bars = ax4.bar(class_names_iris, class_counts, color=['lightblue', 'lightgreen', 'lightcoral'])\n",
    "ax4.set_title('Class Distribution', fontsize=14, weight='bold')\n",
    "ax4.set_ylabel('Number of Samples')\n",
    "\n",
    "# Add value labels\n",
    "for bar, count in zip(bars, class_counts):\n",
    "    ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5, \n",
    "             str(count), ha='center', va='bottom', fontsize=12, weight='bold')\n",
    "\n",
    "# Decision boundary visualization (2D projection)\n",
    "ax5 = plt.subplot(2, 3, 6)\n",
    "\n",
    "# Use the two most important features for 2D visualization\n",
    "top_features = indices[:2]\n",
    "X_2d = X_iris[:, top_features]\n",
    "feature_1_name = feature_names_iris[top_features[0]]\n",
    "feature_2_name = feature_names_iris[top_features[1]]\n",
    "\n",
    "# Create a mesh for decision boundary\n",
    "h = 0.02\n",
    "x_min, x_max = X_2d[:, 0].min() - 1, X_2d[:, 0].max() + 1\n",
    "y_min, y_max = X_2d[:, 1].min() - 1, X_2d[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "# Create a simplified tree for 2D features\n",
    "tree_2d = DecisionTreeClassifier(criterion='gini', max_depth=4, random_state=42)\n",
    "tree_2d.fit(X_2d, y_iris)\n",
    "\n",
    "# Predict on mesh\n",
    "Z = tree_2d.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# Plot decision boundary\n",
    "ax5.contourf(xx, yy, Z, alpha=0.4, cmap='RdYlBu')\n",
    "\n",
    "# Plot data points\n",
    "colors = ['red', 'yellow', 'blue']\n",
    "for i, color in enumerate(colors):\n",
    "    idx = np.where(y_iris == i)\n",
    "    ax5.scatter(X_2d[idx, 0], X_2d[idx, 1], c=color, \n",
    "               label=class_names_iris[i], cmap='RdYlBu', edgecolors='black')\n",
    "\n",
    "ax5.set_xlabel(feature_1_name)\n",
    "ax5.set_ylabel(feature_2_name)\n",
    "ax5.set_title('Decision Boundary (2D Projection)', fontsize=14, weight='bold')\n",
    "ax5.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Analysis of tree depth and performance\n",
    "print(f\"\\nTREE ANALYSIS:\")\n",
    "print(f\"Tree depth: {iris_tree.get_depth()}\")\n",
    "print(f\"Number of leaves: {iris_tree.get_n_leaves()}\")\n",
    "print(f\"Total nodes: {iris_tree.tree_.node_count}\")\n",
    "\n",
    "# Show how tree depth affects performance\n",
    "depths = range(1, 11)\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "for depth in depths:\n",
    "    temp_tree = DecisionTreeClassifier(criterion='gini', max_depth=depth, random_state=42)\n",
    "    temp_tree.fit(X_train, y_train)\n",
    "    \n",
    "    train_score = temp_tree.score(X_train, y_train)\n",
    "    test_score = temp_tree.score(X_test, y_test)\n",
    "    \n",
    "    train_scores.append(train_score)\n",
    "    test_scores.append(test_score)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(depths, train_scores, 'o-', label='Training Accuracy', linewidth=2, markersize=6)\n",
    "plt.plot(depths, test_scores, 's-', label='Test Accuracy', linewidth=2, markersize=6)\n",
    "plt.xlabel('Max Depth')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Decision Tree Performance vs Depth', fontsize=14, weight='bold')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Mark optimal depth\n",
    "optimal_depth = depths[np.argmax(test_scores)]\n",
    "plt.axvline(x=optimal_depth, color='red', linestyle='--', alpha=0.7, \n",
    "           label=f'Optimal Depth: {optimal_depth}')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nINSIGHTS:\")\n",
    "print(f\"• Optimal tree depth: {optimal_depth}\")\n",
    "print(f\"• Best test accuracy: {max(test_scores):.3f}\")\n",
    "print(f\"• Most important feature: {feature_names_iris[indices[0]]}\")\n",
    "print(f\"• Decision trees can achieve {max(test_scores)*100:.1f}% accuracy on Iris!\")\n",
    "\n",
    "print(f\"\\nKEY TAKEAWAYS:\")\n",
    "print(f\"• Gini impurity effectively measures node purity\")\n",
    "print(f\"• Information gain guides optimal splitting decisions\") \n",
    "print(f\"• Tree depth controls model complexity and overfitting\")\n",
    "print(f\"• Feature importance reveals which attributes matter most\")\n",
    "print(f\"• Decision boundaries can be visualized in 2D projections\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "3f36ab92",
   "metadata": {},
   "source": [
    "## Summary: Mastering Decision Trees\n",
    "\n",
    "### **What We've Learned**\n",
    "\n",
    "1. **Gini Impurity Fundamentals**\n",
    "   - Measures how \"mixed\" or \"impure\" a set of examples is\n",
    "   - Formula: `Gini = 1 - Σ(pi)²`\n",
    "   - Range: 0 (pure) to 0.5 (maximum impurity for binary classification)\n",
    "\n",
    "2. **Information Gain**\n",
    "   - Quantifies improvement from a split: `Parent Gini - Weighted Child Gini`\n",
    "   - Guides selection of best splitting feature and value\n",
    "   - Higher gain = better split\n",
    "\n",
    "3. **Decision Tree Algorithm**\n",
    "   - Recursively partition data to maximize information gain\n",
    "   - Stop when nodes are pure, too small, or max depth reached\n",
    "   - Predict using majority class in leaf nodes\n",
    "\n",
    "4. **Implementation Insights**\n",
    "   - Built complete tree from scratch using Gini impurity\n",
    "   - Validated against scikit-learn implementation\n",
    "   - Visualized tree structure and decision paths\n",
    "\n",
    "5. **Real-World Application**\n",
    "   - Applied to Iris classification (96%+ accuracy)\n",
    "   - Analyzed feature importance and decision boundaries\n",
    "   - Explored depth vs performance trade-offs\n",
    "\n",
    "### **Key Advantages of Decision Trees**\n",
    "- **Interpretable**: Easy to understand and explain to stakeholders\n",
    "- **No preprocessing**: Handles categorical and numerical features naturally\n",
    "- **Feature selection**: Automatically identifies important variables\n",
    "- **Non-linear**: Captures complex decision boundaries\n",
    "- **Robust**: Handles outliers and missing values well\n",
    "\n",
    "### **Important Considerations**\n",
    "- **Overfitting**: Deep trees may memorize training data\n",
    "- **Instability**: Small data changes can drastically alter tree structure\n",
    "- **Bias**: Favors features with more levels/splits\n",
    "- **Limited expressiveness**: Struggles with linear relationships\n",
    "\n",
    "### **Next Steps**\n",
    "- **Random Forests**: Combine multiple trees for better performance\n",
    "- **Gradient Boosting**: Sequentially improve weak decision trees\n",
    "- **Pruning techniques**: Reduce overfitting in large trees\n",
    "- **Ensemble methods**: Leverage wisdom of multiple models\n",
    "\n",
    "### **Practical Applications**\n",
    "- **Medical diagnosis**: Symptom-based decision making\n",
    "- **Credit scoring**: Loan approval decisions\n",
    "- **Marketing**: Customer segmentation and targeting\n",
    "- **Quality control**: Defect detection in manufacturing\n",
    "- **Fraud detection**: Identifying suspicious transactions\n",
    "\n",
    "Decision trees are the foundation of many advanced machine learning techniques. Master them, and you'll understand the building blocks of Random Forests, Gradient Boosting, and many other powerful algorithms!"
   ]
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