Classification.ipynb

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    "# data acquisition\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/J0AZZ/artificial-intelligence_studies/master/artificial-intelligence_studies/introducao-ia-UFPB/Prova%201/Stars.csv\")\n",
    "df.info()\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "# approaching categorical variables\n",
    "color_cat = pd.get_dummies(df[\"Color\"])\n",
    "spect_cat = pd.get_dummies(df[\"Spectral_Class\"])\n",
    "X = df[[\"Temperature\", \"L\", \"R\", \"A_M\"]]\n",
    "y = df[\"Type\"]\n",
    "X_normalized = (X-X.mean())/X.std()\n",
    "x = pd.concat(objs=[color_cat, spect_cat, X_normalized], axis=1)\n",
    "\n",
    "# splitting training and validation sets\n",
    "X_test, X_train = x.iloc[200:], x.iloc[:200] \n",
    "y_test, y_train = y.iloc[200:], y.iloc[:200]\n",
    "\n",
    "# model building with two different metrics and 5 values of k\n",
    "for metric in [\"manhattan\", \"euclidean\"]:\n",
    "  print(\"\\n\\nDistance Metric: \", metric, \"\\n\")\n",
    "  print(\"--------------------\")\n",
    "  for k in range(1,6):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k, metric=metric)\n",
    "    knn.fit(X_train, y_train)\n",
    "    print(sk.metrics.classification_report(y_test, knn.predict(X_test)))\n",
    "    print(\"\\nk=\", k, \" Accuracy: \", knn.score(X_test, y_test)*100, \"%\")\n",
    "    print(\"Confusion Matrix:\\n\", sk.metrics.confusion_matrix(y_test, knn.predict(X_test)))\n",
    "  "
   ]
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