Chapter 4 Building Models with Distance Metrics and Nearest Neighbors (Exercise Solution).ipynb

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   "source": [
    "## Practical Exercises with KNN Models\n",
    "\n",
    "### Exercise 1: Building a KNN Classifier\n",
    "Implement a KNN classifier for a binary classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Load the Dataset\n",
    "data = load_breast_cancer()\n",
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "# Preprocess the Data\n",
    "# Split 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=42)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Create and Train the KNN Classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make Predictions\n",
    "y_pred = knn.predict(X_test_scaled)\n",
    "\n",
    "# Evaluate Performance\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.3f}\")\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2: Tuning Hyperparameters with Grid Search\n",
    "Compare the performance of different KNN models using grid search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Load a different dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, \n",
    "                         n_redundant=5, n_classes=3, random_state=42)\n",
    "\n",
    "# Preprocess the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Set up grid search:\n",
    "param_grid = {\n",
    "    'n_neighbors': [3, 5, 7, 9, 11],\n",
    "    'weights': ['uniform', 'distance'],\n",
    "    'metric': ['euclidean', 'manhattan']\n",
    "}\n",
    "knn = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# Fit grid search\n",
    "grid_search.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Evaluate best model\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best cross-validation score:\", grid_search.best_score_)\n",
    "y_pred = grid_search.predict(X_test_scaled)\n",
    "print(\"\\nTest Set Performance:\")\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3 Evaluating a KNN Classifier\n",
    "Evaluate the performance of a KNN classifier using the techniques we've covered in this chapter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "# Load the Dataset\n",
    "# Create a synthetic dataset with 3 classes and some informative/redundant features\n",
    "X, y = make_classification(n_samples=1000, \n",
    "                         n_features=15, \n",
    "                         n_informative=10,\n",
    "                         n_redundant=5, \n",
    "                         n_classes=3,\n",
    "                         n_clusters_per_class=2,\n",
    "                         random_state=42)\n",
    "\n",
    "# Preprocess the Data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Create and Train the KNN Classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make Predictions\n",
    "y_pred = knn.predict(X_test_scaled)\n",
    "\n",
    "# Evaluate Performance\n",
    "print(\"Model Performance Evaluation\")\n",
    "print(\"-\" * 30)\n",
    "print(f\"Accuracy Score: {accuracy_score(y_test, y_pred):.3f}\")\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
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