03_decision_tree.ipynb

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    "### Decision Tree Explained\n",
    "\n",
    "| **Aspect**                   | **Description**                                                                                             |\n",
    "|------------------------------|-------------------------------------------------------------------------------------------------------------|\n",
    "| **Definition**               | A decision tree is a supervised machine learning algorithm that learns a series of hierarchical decisions or rules from data, leading to a prediction. |\n",
    "| **Structure**                | - **Nodes**: Represent decision points based on features in the dataset.<br>- **Edges**: Connect nodes and represent the outcome of a decision.<br>- **Leaves**: Terminal nodes that provide the final prediction or classification. |\n",
    "| **Learning Approach**        | Decision trees learn by recursively splitting the dataset into subsets based on the most significant attribute, maximizing information gain or Gini impurity. |\n",
    "| **Types**                    | - **Classification Trees**: Predict categorical labels or classes.<br>- **Regression Trees**: Predict continuous values or quantities. |\n",
    "| **Advantages**               | - Easy to understand and interpret.<br>- Suitable for both numerical and categorical data.<br>- Can handle nonlinear relationships.<br>- Implicit feature selection. |\n",
    "| **Disadvantages**            | - Prone to overfitting, especially with complex trees.<br>- Sensitive to small variations in the data.<br>- Instability: Small changes in data can lead to large changes in the tree structure. |\n",
    "| **Applications**             | - Predictive modeling in various domains (e.g., healthcare, finance).<br>- Feature selection in machine learning pipelines.<br>- Decision support systems. |\n",
    "| **Splitting Criteria**       | - **Information Gain**: Measures the reduction in entropy or uncertainty after splitting.<br>- **Gini Impurity**: Measures the probability of incorrectly classifying a randomly chosen element. |\n",
    "| **Pruning**                  | Technique to prevent overfitting by removing unnecessary branches (nodes) from the tree.<br>Common methods include cost complexity pruning (minimal cost-complexity pruning) and reduced error pruning. |\n",
    "| **Ensemble Methods**         | - **Random Forests**: Ensemble of decision trees to improve accuracy and robustness.<br>- **Gradient Boosting Machines (GBM)**: Sequentially builds trees, each correcting errors of the previous one. |\n",
    "| **Implementation Libraries** | Popular libraries include scikit-learn (Python), R's rpart, and Weka (Java).                                |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example data\n",
    "data = {\n",
    "    'Age': [25, 45, 35, 50, 23, 37, 32, 28, 40, 27],\n",
    "    'Income': ['High', 'High', 'High', 'Medium', 'Low', 'Low', 'Low', 'Medium', 'Low', 'Medium'],\n",
    "    'Student': ['No', 'No', 'No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No'],\n",
    "    'Buys_Computer': ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes']\n",
    "}\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert categorical features to numeric\n",
    "df['Income'] = df['Income'].map({'Low': 1, 'Medium': 2, 'High': 3})\n",
    "df['Student'] = df['Student'].map({'No': 0, 'Yes': 1})\n",
    "df['Buys_Computer'] = df['Buys_Computer'].map({'No': 0, 'Yes': 1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Independent variables (features) and dependent variable (target)\n",
    "X = df[['Age', 'Income', 'Student']]  # Features\n",
    "y = df['Buys_Computer']                # Target variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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       "  --sklearn-color-text: black;\n",
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       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
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       "div.sk-parallel-item,\n",
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       "#sk-container-id-1 div.sk-parallel-item {\n",
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       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
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       "/* Serial-specific style estimator block */\n",
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       "  padding-left: 1em;\n",
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       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
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       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
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       "  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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       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
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       "#sk-container-id-1 div.sk-toggleable__content {\n",
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       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
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       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "  max-width: 100%;\n",
       "  overflow: auto;\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
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       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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       "#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",
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       "\n",
       "/* Estimator-specific style */\n",
       "\n",
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       "#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",
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       "\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",
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       "  color: var(--sklearn-color-text-on-default-background);\n",
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       "\n",
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       "#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",
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       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
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       "  font-weight: bold;\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>DecisionTreeClassifier(max_depth=3, random_state=0)</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;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating and training the decision tree model\n",
    "model = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=0)\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making predictions\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.0\n",
      "Confusion Matrix:\n",
      "[[0 0]\n",
      " [2 0]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       0.0\n",
      "           1       0.00      0.00      0.00       2.0\n",
      "\n",
      "    accuracy                           0.00       2.0\n",
      "   macro avg       0.00      0.00      0.00       2.0\n",
      "weighted avg       0.00      0.00      0.00       2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred, zero_division=0)  # Add zero_division parameter\n",
    "\n",
    "# Print evaluation results\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Confusion Matrix:\\n{conf_matrix}\")\n",
    "print(f\"Classification Report:\\n{class_report}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the decision tree\n",
    "plt.figure(figsize=(12, 8))\n",
    "plot_tree(model, feature_names=['Age', 'Income', 'Student'], class_names=['No', 'Yes'], filled=True)\n",
    "plt.title('Decision Tree')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml_projects",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}