module-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.  Jelaskan makna dataset. Jelaskan pula makna data training dan data testing!\n",
    "\n",
    "A. Dataset\n",
    "\n",
    "Dataset adalah kumpulan data yang terurut. Saat mengolah data, dataset dapat berupa sekumpulan tabel, skema, dan objek lainnya. Dataset pada dasarnya diatur untuk model terteyang membantu untuk memproses informasi yang dibutuhkan. Dataset juga merupakan kumpulan informasi yang disimpan secara permanen yang biasanya berisi data tingkat kasus, data ydikumpulkan, atau data tingkat panduan statistik.\n",
    "Dataset menggambarkan nilai untuk setiap variabel untuk jumlah yang tidak diketahui seperti tinggi, berat, suhu, volume, dll. dari suatu objek atau nilai angka acak. Nilai-nidalam himpunan ini dikenal sebagai datum. Dataset terdiri dari data dari satu atau lebih elemen yang sesuai dengan setiap baris.\n",
    "\n",
    "B. Data Training\n",
    "\n",
    "Jenis data ini membangun algoritma machine learning. Ilmuwan data/data scientist memasukkan algoritma input data, yang sesuai dengan keluaran yang diharapkan. Model mengevaluasi data berulang kali untuk mempelajari lebih lanjut tentang perilaku data dan kemudian menyesuaikan untuk memenuhi tujuan yang dimaksudkan.\n",
    "\n",
    "C. Data Testing\n",
    "\n",
    "Setelah model dibangun, data testing akan sekali lagi memvalidasi bahwa model tersebut dapat membuat prediksi yang akurat. Jika data training dan validasi menyertakan label untuk memantau metrik kinerja model, data testing sebaiknya tidak diberi label. Data testing memberikan pemeriksaan akhir real-world dari kumpulan data yang tidak terlihat untuk mengonfirmasi bahwa algoritma ML telah dilatih secara efektif."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Jelaskan proses penyusunan dataset\n",
    "\n",
    "a. menentukan sebuah atribut dari suatu data\n",
    "\n",
    "b. memberikan value/element data dari atribut yang tadi telah ditentukan\n",
    "\n",
    "c. menentukan pola apa saja dari sebuah atribut data\n",
    "\n",
    "d. menentukan sebuah kelas yang dihasilkan berdasarkan value atribut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. makna atribut dataset iris dan kelas dataset iris\n",
    "\n",
    "dataset bunga Iris adalah dataset multivariat yang diperkenalkan oleh ahli statistik Inggris dan\n",
    "ahli biologi Ronald Fisher dalam makalahnya tahun 1936 tentang penggunaan beberapa pengukuran dalam masalah taksonomi. Kadang-kadang disebut dataset Iris Anderson karena Edgar Anderson mengumpulkan\n",
    "data untuk mengukur variasi morfologi bunga Iris dari tiga spesies terkait. dataset\n",
    "terdiri dari 50 sampel dari masing-masing tiga spesies Iris (Iris Setosa, Iris virginica, dan Iris\n",
    "versikolor). Empat fitur diukur dari setiap sampel: panjang dan lebar\n",
    "sepal dan petal, dalam sentimeter. Dataset ini menjadi kasus uji khas untuk banyak statistik\n",
    "teknik klasifikasi dalam pembelajaran mesin seperti support vector machine. dataset\n",
    "berisi satu set 150 catatan di bawah 5 atribut - Panjang Petal, Lebar Petal, Panjang Sepal, Sepal\n",
    "lebar dan Kelas (Spesies)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Masukkan dataset iris sebagai array 2D di numpy. Pisahkan setiap kelas di array yang berbeda : setosa, versicolor, virginica, dan unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa \n",
      " [[0.2 1.4 3.5 5.1]\n",
      " [0.2 1.4 3.  4.9]\n",
      " [0.2 1.3 3.2 4.7]\n",
      " [0.2 1.5 3.1 4.6]\n",
      " [0.2 1.4 3.6 5. ]\n",
      " [0.4 1.7 3.9 5.4]\n",
      " [0.3 1.4 3.4 4.6]\n",
      " [0.2 1.5 3.4 5. ]\n",
      " [0.2 1.4 2.9 4.4]\n",
      " [0.1 1.5 3.1 4.9]\n",
      " [0.2 1.5 3.7 5.4]\n",
      " [0.2 1.6 3.4 4.8]\n",
      " [0.1 1.4 3.  4.8]\n",
      " [0.1 1.1 3.  4.3]\n",
      " [0.2 1.2 4.  5.8]\n",
      " [0.4 1.5 4.4 5.7]\n",
      " [0.4 1.3 3.9 5.4]\n",
      " [0.3 1.4 3.5 5.1]\n",
      " [0.3 1.7 3.8 5.7]\n",
      " [0.3 1.5 3.8 5.1]\n",
      " [0.2 1.7 3.4 5.4]\n",
      " [0.4 1.5 3.7 5.1]\n",
      " [0.2 1.  3.6 4.6]\n",
      " [0.5 1.7 3.3 5.1]\n",
      " [0.2 1.9 3.4 4.8]\n",
      " [0.2 1.6 3.  5. ]\n",
      " [0.4 1.6 3.4 5. ]\n",
      " [0.2 1.5 3.5 5.2]\n",
      " [0.2 1.4 3.4 5.2]\n",
      " [0.2 1.6 3.2 4.7]\n",
      " [0.2 1.6 3.1 4.8]\n",
      " [0.4 1.5 3.4 5.4]\n",
      " [0.1 1.5 4.1 5.2]\n",
      " [0.2 1.4 4.2 5.5]\n",
      " [0.2 1.5 3.1 4.9]\n",
      " [0.2 1.2 3.2 5. ]\n",
      " [0.2 1.3 3.5 5.5]\n",
      " [0.1 1.4 3.6 4.9]\n",
      " [0.2 1.3 3.  4.4]\n",
      " [0.2 1.5 3.4 5.1]\n",
      " [0.3 1.3 3.5 5. ]\n",
      " [0.3 1.3 2.3 4.5]\n",
      " [0.2 1.3 3.2 4.4]\n",
      " [0.6 1.6 3.5 5. ]\n",
      " [0.4 1.9 3.8 5.1]] \n",
      "\n",
      "verginica \n",
      " [[2.5 6.  3.3 6.3]\n",
      " [1.9 5.1 2.7 5.8]\n",
      " [2.1 5.9 3.  7.1]\n",
      " [1.8 5.6 2.9 6.3]\n",
      " [2.2 5.8 3.  6.5]\n",
      " [2.1 6.6 3.  7.6]\n",
      " [1.7 4.5 2.5 4.9]\n",
      " [1.8 6.3 2.9 7.3]\n",
      " [1.8 5.8 2.5 6.7]\n",
      " [2.5 6.1 3.6 7.2]\n",
      " [2.  5.1 3.2 6.5]\n",
      " [1.9 5.3 2.7 6.4]\n",
      " [2.1 5.5 3.  6.8]\n",
      " [2.  5.  2.5 5.7]\n",
      " [2.4 5.1 2.8 5.8]\n",
      " [2.3 5.3 3.2 6.4]\n",
      " [1.8 5.5 3.  6.5]\n",
      " [2.2 6.7 3.8 7.7]\n",
      " [2.3 6.9 2.6 7.7]\n",
      " [1.5 5.  2.2 6. ]\n",
      " [2.3 5.7 3.2 6.9]\n",
      " [2.  4.9 2.8 5.6]\n",
      " [2.  6.7 2.8 7.7]\n",
      " [1.8 4.9 2.7 6.3]\n",
      " [2.1 5.7 3.3 6.7]\n",
      " [1.8 6.  3.2 7.2]\n",
      " [1.8 4.8 2.8 6.2]\n",
      " [1.8 4.9 3.  6.1]\n",
      " [2.1 5.6 2.8 6.4]\n",
      " [1.6 5.8 3.  7.2]\n",
      " [1.9 6.1 2.8 7.4]\n",
      " [2.  6.4 3.8 7.9]\n",
      " [2.2 5.6 2.8 6.4]\n",
      " [1.5 5.1 2.8 6.3]\n",
      " [1.4 5.6 2.6 6.1]\n",
      " [2.3 6.1 3.  7.7]\n",
      " [2.4 5.6 3.4 6.3]\n",
      " [1.8 5.5 3.1 6.4]\n",
      " [1.8 4.8 3.  6. ]\n",
      " [2.1 5.4 3.1 6.9]\n",
      " [2.4 5.6 3.1 6.7]\n",
      " [2.3 5.1 3.1 6.9]\n",
      " [1.9 5.1 2.7 5.8]\n",
      " [2.3 5.9 3.2 6.8]\n",
      " [2.5 5.7 3.3 6.7]] \n",
      "\n",
      "versicolor \n",
      " [[1.4 4.7 3.2 7. ]\n",
      " [1.5 4.5 3.2 6.4]\n",
      " [1.5 4.9 3.1 6.9]\n",
      " [1.3 4.  2.3 5.5]\n",
      " [1.5 4.6 2.8 6.5]\n",
      " [1.3 4.5 2.8 5.7]\n",
      " [1.6 4.7 3.3 6.3]\n",
      " [1.  3.3 2.4 4.9]\n",
      " [1.3 4.6 2.9 6.6]\n",
      " [1.4 3.9 2.7 5.2]\n",
      " [1.  3.5 2.  5. ]\n",
      " [1.5 4.2 3.  5.9]\n",
      " [1.  4.  2.2 6. ]\n",
      " [1.4 4.7 2.9 6.1]\n",
      " [1.3 3.6 2.9 5.6]\n",
      " [1.4 4.4 3.1 6.7]\n",
      " [1.5 4.5 3.  5.6]\n",
      " [1.  4.1 2.7 5.8]\n",
      " [1.5 4.5 2.2 6.2]\n",
      " [1.1 3.9 2.5 5.6]\n",
      " [1.8 4.8 3.2 5.9]\n",
      " [1.3 4.  2.8 6.1]\n",
      " [1.5 4.9 2.5 6.3]\n",
      " [1.2 4.7 2.8 6.1]\n",
      " [1.3 4.3 2.9 6.4]\n",
      " [1.4 4.4 3.  6.6]\n",
      " [1.4 4.8 2.8 6.8]\n",
      " [1.7 5.  3.  6.7]\n",
      " [1.5 4.5 2.9 6. ]\n",
      " [1.  3.5 2.6 5.7]\n",
      " [1.1 3.8 2.4 5.5]\n",
      " [1.  3.7 2.4 5.5]\n",
      " [1.2 3.9 2.7 5.8]\n",
      " [1.6 5.1 2.7 6. ]\n",
      " [1.5 4.5 3.  5.4]\n",
      " [1.6 4.5 3.4 6. ]\n",
      " [1.5 4.7 3.1 6.7]\n",
      " [1.3 4.4 2.3 6.3]\n",
      " [1.3 4.1 3.  5.6]\n",
      " [1.3 4.  2.5 5.5]\n",
      " [1.2 4.4 2.6 5.5]\n",
      " [1.4 4.6 3.  6.1]\n",
      " [1.2 4.  2.6 5.8]\n",
      " [1.  3.3 2.3 5. ]\n",
      " [1.3 4.2 2.7 5.6]] \n",
      "\n",
      "unknown \n",
      " [[0.3 1.4 3.  4.8]\n",
      " [0.2 1.6 3.8 5.1]\n",
      " [0.2 1.4 3.2 4.6]\n",
      " [0.2 1.5 3.7 5.3]\n",
      " [0.2 1.4 3.3 5. ]\n",
      " [1.2 4.2 3.  5.7]\n",
      " [1.3 4.2 2.9 5.7]\n",
      " [1.3 4.3 2.9 6.2]\n",
      " [1.1 3.  2.5 5.1]\n",
      " [1.3 4.1 2.8 5.7]\n",
      " [2.3 5.2 3.  6.7]\n",
      " [1.9 5.  2.5 6.3]\n",
      " [2.  5.2 3.  6.5]\n",
      " [2.3 5.4 3.4 6.2]\n",
      " [1.8 5.1 3.  5.9]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "setosa = np.array([\n",
    "[0.2,\t1.4,\t3.5,\t5.1],\n",
    "[0.2,\t1.4,\t3,\t4.9],\n",
    "[0.2,\t1.3,\t3.2,\t4.7],\n",
    "[0.2,\t1.5,\t3.1,\t4.6],\n",
    "[0.2,\t1.4,\t3.6,\t5],\n",
    "[0.4,\t1.7,\t3.9,\t5.4],\n",
    "[0.3,\t1.4,\t3.4,\t4.6],\n",
    "[0.2,\t1.5,\t3.4,\t5],\n",
    "[0.2,\t1.4,\t2.9,\t4.4],\n",
    "[0.1,\t1.5,\t3.1,\t4.9],\n",
    "[0.2,\t1.5,\t3.7,\t5.4],\n",
    "[0.2,\t1.6,\t3.4,\t4.8],\n",
    "[0.1,\t1.4,\t3,\t4.8],\n",
    "[0.1,\t1.1,\t3,\t4.3],\n",
    "[0.2,\t1.2,\t4,\t5.8],\n",
    "[0.4,\t1.5,\t4.4,\t5.7],\n",
    "[0.4,\t1.3,\t3.9,\t5.4],\n",
    "[0.3,\t1.4,\t3.5,\t5.1],\n",
    "[0.3,\t1.7,\t3.8,\t5.7],\n",
    "[0.3,\t1.5,\t3.8,\t5.1],\n",
    "[0.2,\t1.7,\t3.4,\t5.4],\n",
    "[0.4,\t1.5,\t3.7,\t5.1],\n",
    "[0.2,\t1,\t3.6,\t4.6],\n",
    "[0.5,\t1.7,\t3.3,\t5.1],\n",
    "[0.2,\t1.9,\t3.4,\t4.8],\n",
    "[0.2,\t1.6,\t3,\t5],\n",
    "[0.4,\t1.6,\t3.4,\t5],\n",
    "[0.2,\t1.5,\t3.5,\t5.2],\n",
    "[0.2,\t1.4,\t3.4,\t5.2],\n",
    "[0.2,\t1.6,\t3.2,\t4.7],\n",
    "[0.2,\t1.6,\t3.1,\t4.8],\n",
    "[0.4,\t1.5,\t3.4,\t5.4],\n",
    "[0.1,\t1.5,\t4.1,\t5.2],\n",
    "[0.2,\t1.4,\t4.2,\t5.5],\n",
    "[0.2,\t1.5,\t3.1,\t4.9],\n",
    "[0.2,\t1.2,\t3.2,\t5],\n",
    "[0.2,\t1.3,\t3.5,\t5.5],\n",
    "[0.1,\t1.4,\t3.6,\t4.9],\n",
    "[0.2,\t1.3,\t3,\t4.4],\n",
    "[0.2,\t1.5,\t3.4,\t5.1],\n",
    "[0.3,\t1.3,\t3.5,\t5],\n",
    "[0.3,\t1.3,\t2.3,\t4.5],\n",
    "[0.2,\t1.3,\t3.2,\t4.4],\n",
    "[0.6,\t1.6,\t3.5,\t5],\n",
    "[0.4,\t1.9,\t3.8,\t5.1]\n",
    "])\n",
    "\n",
    "verginica = np.array([\n",
    "[2.5,\t6,\t3.3,\t6.3],\n",
    "[1.9,\t5.1,\t2.7,\t5.8],\n",
    "[2.1,\t5.9,\t3,\t7.1],\n",
    "[1.8,\t5.6,\t2.9,\t6.3],\n",
    "[2.2,\t5.8,\t3,\t6.5],\n",
    "[2.1,\t6.6,\t3,\t7.6],\n",
    "[1.7,\t4.5,\t2.5,\t4.9],\n",
    "[1.8,\t6.3,\t2.9,\t7.3],\n",
    "[1.8,\t5.8,\t2.5,\t6.7],\n",
    "[2.5,\t6.1,\t3.6,\t7.2],\n",
    "[2,\t5.1,\t3.2,\t6.5],\n",
    "[1.9,\t5.3,\t2.7,\t6.4],\n",
    "[2.1,\t5.5,\t3,\t6.8],\n",
    "[2,\t5,\t2.5,\t5.7],\n",
    "[2.4,\t5.1,\t2.8,\t5.8],\n",
    "[2.3,\t5.3,\t3.2,\t6.4],\n",
    "[1.8,\t5.5,\t3, 6.5],\n",
    "[2.2,\t6.7,\t3.8,\t7.7],\n",
    "[2.3,\t6.9,\t2.6,\t7.7],\n",
    "[1.5,\t5,\t2.2,\t6],\n",
    "[2.3,\t5.7,\t3.2,\t6.9],\n",
    "[2,\t4.9,\t2.8,\t5.6],\n",
    "[2,\t6.7,\t2.8,\t7.7],\n",
    "[1.8,\t4.9,\t2.7,\t6.3],\n",
    "[2.1,\t5.7,\t3.3,\t6.7],\n",
    "[1.8,\t6,\t3.2,\t7.2],\n",
    "[1.8,\t4.8,\t2.8,\t6.2],\n",
    "[1.8,\t4.9,\t3,\t6.1],\n",
    "[2.1,\t5.6,\t2.8,\t6.4],\n",
    "[1.6,\t5.8,\t3,\t7.2],\n",
    "[1.9,\t6.1,\t2.8,\t7.4],\n",
    "[2,\t6.4,\t3.8,\t7.9],\n",
    "[2.2,\t5.6,\t2.8,\t6.4],\n",
    "[1.5,\t5.1,\t2.8,\t6.3],\n",
    "[1.4,\t5.6,\t2.6,\t6.1],\n",
    "[2.3,\t6.1,\t3,\t7.7],\n",
    "[2.4,\t5.6,\t3.4,\t6.3],\n",
    "[1.8,\t5.5,\t3.1,\t6.4],\n",
    "[1.8,\t4.8,\t3,\t6],\n",
    "[2.1,\t5.4,\t3.1,\t6.9],\n",
    "[2.4,\t5.6,\t3.1,\t6.7],\n",
    "[2.3,\t5.1,\t3.1,\t6.9],\n",
    "[1.9,\t5.1,\t2.7,\t5.8],\n",
    "[2.3,\t5.9,\t3.2,\t6.8],\n",
    "[2.5,\t5.7,\t3.3,\t6.7]\n",
    "])\n",
    "\n",
    "versicolor = np.array([\n",
    "[1.4,\t4.7,\t3.2,\t7],\n",
    "[1.5,\t4.5,\t3.2,\t6.4],\n",
    "[1.5,\t4.9,\t3.1,\t6.9],\n",
    "[1.3,\t4,\t2.3,\t5.5],\n",
    "[1.5,\t4.6,\t2.8,\t6.5],\n",
    "[1.3,\t4.5,\t2.8,\t5.7],\n",
    "[1.6,\t4.7,\t3.3,\t6.3],\n",
    "[1,\t3.3,\t2.4,\t4.9],\n",
    "[1.3,\t4.6,\t2.9,\t6.6],\n",
    "[1.4,\t3.9,\t2.7,\t5.2],\n",
    "[1,\t3.5,\t2,\t5],\n",
    "[1.5,\t4.2,\t3,\t5.9],\n",
    "[1,\t4,\t2.2,\t6],\n",
    "[1.4,\t4.7,\t2.9,\t6.1],\n",
    "[1.3,\t3.6,\t2.9,\t5.6],\n",
    "[1.4,\t4.4,\t3.1,\t6.7],\n",
    "[1.5,\t4.5,\t3,\t5.6],\n",
    "[1,\t4.1,\t2.7,\t5.8],\n",
    "[1.5,\t4.5,\t2.2,\t6.2],\n",
    "[1.1,\t3.9,\t2.5,\t5.6],\n",
    "[1.8,\t4.8,\t3.2,\t5.9],\n",
    "[1.3,\t4,\t2.8,\t6.1],\n",
    "[1.5,\t4.9,\t2.5,\t6.3],\n",
    "[1.2,\t4.7,\t2.8,\t6.1],\n",
    "[1.3,\t4.3,\t2.9,\t6.4],\n",
    "[1.4,\t4.4,\t3,\t6.6],\n",
    "[1.4,\t4.8,\t2.8,\t6.8],\n",
    "[1.7,\t5,\t3,\t6.7],\n",
    "[1.5,\t4.5,\t2.9,\t6],\n",
    "[1,\t3.5,\t2.6,\t5.7],\n",
    "[1.1,\t3.8,\t2.4,\t5.5],\n",
    "[1,\t3.7,\t2.4,\t5.5],\n",
    "[1.2,\t3.9,\t2.7,\t5.8],\n",
    "[1.6,\t5.1,\t2.7,\t6],\n",
    "[1.5,\t4.5,\t3,\t5.4],\n",
    "[1.6,\t4.5,\t3.4,\t6],\n",
    "[1.5,\t4.7,\t3.1,\t6.7],\n",
    "[1.3,\t4.4,\t2.3,\t6.3],\n",
    "[1.3,\t4.1,\t3,\t5.6],\n",
    "[1.3,\t4,\t2.5,\t5.5],\n",
    "[1.2,\t4.4,\t2.6,\t5.5],\n",
    "[1.4,\t4.6,\t3,\t6.1],\n",
    "[1.2,\t4,\t2.6,\t5.8],\n",
    "[1,\t3.3,\t2.3,\t5],\n",
    "[1.3,\t4.2,\t2.7,\t5.6]                       \n",
    "])\n",
    "\n",
    "unknown = np.array([\n",
    "[0.3,\t1.4,\t3,\t4.8],\n",
    "[0.2,\t1.6,\t3.8,\t5.1],\n",
    "[0.2,\t1.4,\t3.2,\t4.6],\n",
    "[0.2,\t1.5,\t3.7,\t5.3],\n",
    "[0.2,\t1.4,\t3.3,\t5],\n",
    "[1.2,\t4.2,\t3,\t5.7],\n",
    "[1.3,\t4.2,\t2.9,\t5.7],\n",
    "[1.3,\t4.3,\t2.9,\t6.2],\n",
    "[1.1,\t3,\t2.5,\t5.1],\n",
    "[1.3,\t4.1,\t2.8,\t5.7],\n",
    "[2.3,\t5.2,\t3,\t6.7],\n",
    "[1.9,\t5,\t2.5,\t6.3],\n",
    "[2,\t5.2,\t3,\t6.5],\n",
    "[2.3,\t5.4,\t3.4,\t6.2],\n",
    "[1.8,\t5.1,\t3,\t5.9]                    \n",
    "])\n",
    "\n",
    "print(\"setosa \\n\", setosa, \"\\n\")\n",
    "print(\"verginica \\n\", verginica, \"\\n\")\n",
    "print(\"versicolor \\n\", versicolor, \"\\n\")\n",
    "print(\"unknown \\n\", unknown, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5.\tBuatlah scatter plot yang menggunakan pasangan attribut berikut pada setiap kelas (termasuk unknown). Berikan warna merah untuk setosa, hijau untuk versicolor, biru untuk virginica, dan kuning untuk unknown \n",
    "\n",
    "A.\tSepal length [3] dan petal width [0]\n",
    "\n",
    "B.\tSepal width [2] petal length [1]\n",
    "\n",
    "C.\tSepal width [2] dan petal width [0]\n",
    "\n",
    "D.\tSepal length [3] dan petal length [1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20f3f8ee310>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \n",
    "\n",
    "a1 = setosa[:,3]\n",
    "a2 = setosa[:,1]\n",
    "b1 = verginica[:,3]\n",
    "b2 = verginica[:,1]\n",
    "c1 = versicolor[:,3]\n",
    "c2 = versicolor[:,1]\n",
    "d1 = unknown[:,3]\n",
    "d2 = unknown[:,1]\n",
    "\n",
    "plt.scatter(a1, a2, color='red')\n",
    "plt.scatter(b1, b2, color='blue')\n",
    "plt.scatter(c1, c2, color='green')\n",
    "plt.scatter(d1, d2, color='yellow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.\tJelaskan bagaimana karakter pengelompokan setiap kelas pada scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20f3f71dd60>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pw = verginica[:,0]\n",
    "pl = verginica[:,1]\n",
    "sw = verginica[:,2]\n",
    "sl = verginica[:,3]\n",
    "\n",
    "plt.scatter(sl, pw, color='blue')\n",
    "plt.scatter(sw, pl, color='blue')\n",
    "plt.scatter(sw, pw, color='blue')\n",
    "plt.scatter(sl, pl, color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.\tPerhatikan data yang berjenis unknown. Jelaskan bagaimana cara meramal jenis data unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20f3f796670>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD4CAYAAAATpHZ6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdDElEQVR4nO3dbWxk1XkH8P/fMxs1AwmpulbJsjBDKpooJCVhLQJJFK1qp+IlL1/ygdQVKmrlMoYqlEKVJhIEpP3UiqAEvBsr0BbZIWrzQggsTbI0UXcVhcZLeA2h2gZ711kSDFV2u7tRwPbTD/faa4/vzDnjOXPn3jv/nzSy77l37318PPvs7DnPuZdmBhERKYaBXgcgIiLhKKmLiBSIkrqISIEoqYuIFIiSuohIgZR7deGtW7darVbr1eVFRHLp4MGDr5jZYLP9PUvqtVoNMzMzvbq8iEgukZxrtV/DLyIiBaKkLiJSIErqIiIFoqQuIlIgSuoiIgXiTOokzyX5fZLPk3yO5KcSjtlJ8hjJJ+PXrd0JVyT7pqeBWg0YGIi+Tk/3Zwy+8hRrHviUNC4C+Fsze4LkmwAcJPk9M/tpw3H7zewj4UMUyY/paWBsDDh1Ktqem4u2AWB0tH9i8JWnWPOC7d56l+S3ANxtZt9b07YTwM3tJPWhoSFTnboUTa0WJaZG1SowO9s/MfjKU6xZQfKgmQ0129/WmDrJGoD3Ang8YfdlJJ8i+SjJC5v8+TGSMyRnFhYW2rm0SC4cPtxee1Fj8JWnWPPCO6mTPBPA1wHcaGbHG3Y/AaBqZhcB+CKAB5POYWaTZjZkZkODg01XuYrk1nnntdde1Bh85SnWvPBK6iS3IEro02b2jcb9ZnbczE7E3+8FsIXk1qCRiuTArl1ApbK+rVKJ2vspBl95ijUvfKpfCOBeAM+b2Z1Njjk7Pg4kL4nP+2rIQEXyYHQUmJyMxoTJ6OvkZLqTflmIwVeeYs0L50QpyQ8C2A/gGQDLcfNnAJwHAGa2h+QNAOqIKmV+A+AmM/thq/NqolREpH0dT5Sa2QEzo5n9kZm9J37tNbM9ZrYnPuZuM7vQzC4ys0tdCV2kXXmqZc5CrFmIQXqjZ7feFfGVp1rmLMSahRikd9quUw9Fwy/iK0+1zFmINQsxSPcErVMX6YU81TJnIdYsxCC9o6QumZenWuYsxJqFGKR3lNQl8/JUy5yFWLMQg/SOkrpkXp5qmbMQaxZikN7RRKmISI5oolREpI8oqYu0ZRpADdFfnVq83eYZUlkY5I7TFcf4OFAuR0M45XK03c9y0x9m1pPXjh07TCRfpsysYuvfypW43fMMU2aVihlw+lWpRO1pxumKo15fv2/lVa+HjDM/stQfAGasRW7VmLqItxqAhFU9qAKY9TtDLY2FQTW44nTFUS4DS0sb95dKwOJiqDjzI0v9oTF1kWCard7xX9WTzsIgd5yuOJISWKv2ostTfyipi3hrtnrHf1VPOguD3HG64iiVkvc3ay+6PPWHkrqIt10AGlb1oBK3e54hlYVB7jhdcazcAKxRs/aiy1V/tBpw7+ZLE6WST1NmVrXobtRVa2eSdPUMU2bVqhkZfQ07Sbp6FXPF6YqjXjcrlaIJwVKpfydJV2SlP6CJUhGR4tBEqUjGuOrD9YCLbMrL70UPyRBJkesBFnrARTbl6fei4ReRFLnqw/WAi2zK0u9Fwy8iGeKqD9cDLrIpT78XJXWRFLnqw/WAi2zK0+9FSV0kRa76cD3gIpvy9HtRUhdJkesBFnrARTbl6feiiVIRkRzRRKlIxqRR7+xzjTTq5fNS252XOL20Wm7azZduEyD9KI37qftcw3VMiDjTuXd85/IS5wroNgEi2ZFGvbPPNdKol89SbXcreYlzhWv4RUldJEUDA9FnwUYksLyc3jVcx4SIM42fNYS8xLlCY+oiGZJGvbPPNdKol89LbXde4vSlpC6SojTqnX2ukUa9fF5qu/MSp7dWA+7dfGmiVPpVGvdT97mG65gQcaZz7/jO5SVOM02UiogUSsdj6iTPJfl9ks+TfI7kpxKOIckvkDxE8mmSF3cauBRHiJrpfnLgwDTm52tYXh7A/HwNBw70cWdkSG7q9lt9jI8/xb8VwMXx928C8N8A3tlwzJUAHgVAAJcCeNx1Xg2/9IcQNdP9ZP/+KTtxomJr/7qcOFGx/fv7sDMyJEt1+wg9/ELyWwDuNrPvrWn7EoAfmNkD8fYLAHaa2UvNzqPhl/4Qoma6n8zP17B9+8bOmJ+vYvv22fQDEgDZqtsPWtJIsgbgvQAeb9h1DoAja7bn47bGPz9GcobkzMLCQjuXlpzyuQ91nu5V3W3btiX/0M3aJR0h3qNpvc+9kzrJMwF8HcCNZna8cXfCH9nwXwAzmzSzITMbGhwcbC9SyaUQNdP95OjR5B+6WbukI091+15JneQWRAl92sy+kXDIPIBz12xvB3C08/Ak70LUTPeT2dldOHlyfWecPFnB7GwfdkaG5Kpuv9WAezzeTgD3A7irxTFXYf1E6X+5zquJ0v4Roma6n+zfP2VHjlRtaYl25EhVk6QZkZW6fXQ6UUrygwD2A3gGwMqdED4D4Lz4H4U9JAngbgCXAzgF4FozazkLqolSEZH2uSZKy64TmNkBJI+Zrz3GAFzffngiIhKS7v0iuTA+DpTL0Z3zyuVoO7umAdQQ/fWqxdtr9mqhVXDq09Ocn9RFem18HNi9+/T20tLp7YmJ3sTU3DSAMUSjkAAwF28DwCimp4GxMeBUvHtuLtoGsvm8yzxQn66ne79I5pXLUSJvVCoBi4vpx9NaDVEib1QFMKuFVl3Qb32q+6lL7iUl9FbtvdVsJUnUroVW4alP11NSl8wrldpr761mK0midi20Ck99up6SumTeyviob3tv7QLQsMIElbhdC626QX26npK6ZN7EBFCvn/5kXipF29mbJAWAUQCTiMbQGX+djNujibvJyWi8l4y+Tk7254ReKOrT9TRRKiKSI5oolb5x4PA45o+XsWzE/PEyDhxeX8we4mEdPudw1dSHuIZLnuq20+iPvtLqHgLdfOneLxLS/rm6nfjt+rfZid/C9s/VzSzMwzp8zlGvr9+/8qrXw13DJU8PHUmjP4oGjnu/KKlLIRw5VrKkt1rUHt08KSnZVqunz+E6xuccpVLyMaVSuGu4hDhHWtLoj6JxJXWNqUshLBsxkHCHomUDBmgYGIjSQSMSWI5vU+c6xuccbHGXJLMw13AJcY60pNEfRaMxdekLR/8vuWh9pT3Ewzp8zuGqqQ9xDZc81W2n0R/9RkldCmH212M4+dr6tpOvRe1AmId1+JzDVVMf4houearbTqM/+k6rsZluvjSmLqHtn6vbkWMlW1qOxtJXJklXhHhYh8856vXTY+ul0ulJ0pDXcMnTQ0fS6I8igcbURUSKQ2PqGaXaW+mm6WemUburhoHbB1C7q4bpZ/QG6xe6n3oP6P7P0k3Tz0xj7NtjOPV69AabOzaHsW9Hb7DRd+sNVnQafumBfrv/s6SrdlcNc8c2vsGqZ1Uxe+Ns+gFJUBp+ySDd/1m66fCx5DdSs3YpFiX1HlDtrXTTeWclv5GatUuxKKn3gGpvpZt2De9CZcv6N1hlSwW7hvUG6wdK6j2g+z9LN42+exSTH51E9awqCKJ6VhWTH53UJGmf0ESpiEiOaKJUJDb+yDjKd5TB24nyHWWMPzLu/kOboBrx9mjNRliqU5e+MP7IOHbP7F7dXrKl1e2Jq8I9F0814u3Rmo3wNPwifaF8RxlLtrShvcQSFm9dDHYd1Yi3R2s22qfhFxEgMaG3at8s1Yi3R2s2wlNSl75QYvKNzpu1b5ZqxNujNRvhKalLXxjbkXyj82btm6Ua8fZozUZ4SurSFyaumkB9qL76ybzEEupD9aCTpIBqxNulNRvhaaJURCRHOp4oJXkfyZdJPttk/06Sx0g+Gb9u7SRgERHZPJ/hl38GcLnjmP1m9p74dUfnYUlWhFhIk9ZiHNd1xndPo3xLDfzcAMq31DC+uzs/i+s6rnNkpc+1iCqfnIuPzOw/SdZSiEUyJsRCmrQW47iuM757Grt/MQacGe1fOnMu2t4NTNTD/Syu67jOkZU+1yKq/PIaU4+T+sNm9q6EfTsBfB3APICjAG42s+dc59SYevaFWEiT1mIc13XKt9SwdObG/aUTVSz+g18cPj+L6zquc2Slz7WIKrtcY+ohbhPwBICqmZ0geSWABwFc0CSYMQBjAHCeClEzL8RCmrQW47ius3RG8v5m7Zu5hs91XOfISp9rEVV+dVzSaGbHzexE/P1eAFtIbm1y7KSZDZnZ0ODgYKeXli4LsZAmrcU4ruuUTibvb9a+mWv4XMd1jqz0uRZR5VfHSZ3k2SQZf39JfM5XOz2v9F6IhTRpLcZxXWfsbbuA1xtWubxeidoDXcPnOq5zZKXPtYgqx8ys5QvAAwBeAvA6onHzvwBwHYDr4v03AHgOwFMAfgTg/a5zmhl27Nhhkn1TT09Z9fNV4+do1c9XberpqZ6cI8R16hNTVrq5ariNVrq5avWJ7vwsruu4zpGVPk/r9ybtATBjLXKrFh+JiOSI7tLY51Rr3J6RO8fB28rg5wjeVsbInd15kEanQtS6q5a9mPRJvcAaa42BaFxU9yJJNnLnOB47vhvgmkYDht9cx76bwt4jphOu36vP7z3Ee0Pvr95wfVJXUi8w1Rq3h7eVgYGE+6svl2C3h3uQRqdC1Lqrlj2/NPzSx1Rr3CY2eWBGs/YeCVHrrlr24lJSLzDVGrfJmjwwo1l7j4SodVcte3EpqReYao3bM3zWGNA4Gmlxe4aEqHVXLXtxKakXmB7Y0J59N01g+M11YLkUJfflUuYmSQH379Xn9x7ivaH3VzZpolREJEc0USpdN/7IOMp3lMHbifIdZYw/sr62e+T+EfB2rr5G7h/ZcI6fvTKCxWXCjFhcJn72ysZj0uCuu54GUEP0V6cWb7d3DtWHSzfpk7p0ZPyRceye2b2hfeX5nyP3j+CxFx/bsH/4/GHsu2YfgCihv/33HgPX1IebAS+8Oox3bN3XtdgbueuupxHdZPTUmj9VATAJwK/+W/Xh0inVqUtXle8oY8k2lvyVWMLirYvg7Uz4UxG7LXrvLS4T5YT/My4uA+WB9N6f7rrrGoCN+4EqgFmvc6g+XDql4RfpqqSE3qo9SalJ3m/W3i3uuutm9df+9d+qD5duU1KXjpSYXMPdrD3JUpMP483au8Vdd92s/tq//lv14dJtSurSkbEdyTXcK+3D5w8n7l/bfuh/h9E4CmgWtafJXXe9C9EY+roj4na/c6g+XLpNSV06MnHVBOpD9dVP5iWWVidJAWDfNfs2JPa1k6QA8I6t+/DCq8NYXI6S+eJy+pOkgE/d9SiiSdEqort+VbF2ktTnHKoPl27TRKmISI5oolS6LkzNdOv6b1ctfCiq/5a8K/c6AMm3xprpuWNzGPt2NJ7uPxzQWP89F28DwOiGWvglW1rdXhnmCSHMzyLSWxp+kY6EqZmuoVX9t6sWPhTVf0seaPhFuipMzXTr+u8QtfBeUaj+WwpASV06EqZmunX9d4haeK8oVP8tBaCkLh0JUzPduv7bVQsfiuq/pQiU1KUjYWqmW9d/u2rhs/WziPSWJkpFRHJEE6UiIn1EST3X3A9sSCWKFB4KISJ+tPgot1ov2EktCseCHS3oEUmXxtRzqwbXAxtSiSKFh0KIyGkaUy8s9wMbUokihYdCiIg/JfXccj+wIZUoUngohIj4U1LPLfcDG1KJIoWHQoiIPyX13HI/sCGVKFJ4KISI+NNEqYhIjnQ8UUryPpIvk3y2yX6S/ALJQySfJnlxJwH3C5/a7bQeDNGpEHXoadS6q15e+oFPnfo/A7gbwP1N9l8B4IL49T4Au+Ov0oRP7XZaD4boVIg69DRq3VUvL/3Ca/iFZA3Aw2b2roR9XwLwAzN7IN5+AcBOM3up1Tn7efjFp3Y7rQdDdCpEHXoate6ql5eiSKNO/RwAR9Zsz8dtScGMkZwhObOwsBDg0vnkU7ud1oMhOhWiDj2NWnfVy0u/CJHUmdCW+PHfzCbNbMjMhgYHBwNcOp98arfTejBEp0LUoadR6656eekXIZL6PIBz12xvB3A0wHkLy6d2O60HQ3QqRB16GrXuqpeXfhEiqT8E4Jq4CuZSAMdc4+n9zqd2O60HQ3QqRB16GrXuqpeXfuGcKCX5AICdALYC+BWA2wBsAQAz20OSiKpjLkd0y8Brzcw5A9rPE6UiIpvlmih1ljSa2Scd+w3A9ZuITUREAtNtAkRECkRJXUSkQJTURUQKREldRKRAlNRFRApESV1EpECU1EVECkRJXUSkQJTURUQKREldRKRAlNRFRApESV1EpECU1EVECkRJXUSkQJTURUQKREldRKRAlNRFRApESV1EpECU1EVECkRJXUSkQJTURUQKREldRKRAlNRFRApESV1EpECU1LNsehqo1YCBgejr9HSvIxKRjCv3OgBpYnoaGBsDTp2Ktufmom0AGB3tXVwikmn6pJ5Vn/3s6YS+4tSpqF1EpAkl9aw6fLi9dhERKKln13nntdcuIgIl9ezatQuoVNa3VSpRu4hIE0rqWTU6CkxOAtUqQEZfJyc1SSoiLan6JctGR5XERaQtXp/USV5O8gWSh0h+OmH/TpLHSD4Zv24NH6psoDp2EWng/KROsgTgHgAfBjAP4MckHzKznzYcut/MPtKFGCWJ6thFJIHPJ/VLABwys5+b2WsAvgrg490NS5xUxy4iCXyS+jkAjqzZno/bGl1G8imSj5K8MOlEJMdIzpCcWVhY2ES4skp17CKSwCepM6HNGrafAFA1s4sAfBHAg0knMrNJMxsys6HBwcG2ApUGqmMXkQQ+SX0ewLlrtrcDOLr2ADM7bmYn4u/3AthCcmuwKGUj1bGLSAKfpP5jABeQPJ/kGwBcDeChtQeQPJsk4+8vic/7auhgZQ3VsYtIAmf1i5ktkrwBwHcAlADcZ2bPkbwu3r8HwCcA1EkuAvgNgKvNrHGIRkJTHbuINPCqUzezvWb2h2b2B2a2K27bEyd0mNndZnahmV1kZpea2Q+7GXTfcNWhj48D5XL0Sb1cjrZDGxmJzr/yGhkJfw1ANfcioZhZT147duwwaWFqyqxSMQNOvyqVqN3MrF5fv2/lVa+Hi2F4OPkaw8PhrmHm/llFZBWAGWuRW2k9GiUZGhqymZmZnlw7F2q1aEFRo2oVmJ2NPpkvLW3cXyoBi4thYmBS4VMs5PvG9bOKyCqSB81sqNl+3dArq1x16EkJvVV7lqnmXiQYJfWsctWhl0rJ+5u1Z5lq7kWCUVLPKlcd+sp9Xho1a9+M4eH22jdLNfciwSipZ5WrDn1iAqjXT38yL5Wi7YmJcDHs27cxgQ8PR+0hqeZeJBhNlIqI5IgmShv51EMHqZmeBlBD1MW1eDtwrGnUqadFdeoiYbSqd+zmqyd16j710EFqpqfMrGLrf+RK3B4o1jTq1NOiOnURb1Cd+ho+9dBBaqZrABLOgSoAz3NkoU49LapTF/HmGn7pr6Q+MJC8aIYElpf9j3FfCBvvTgxEdzH2PIcrjrQWBqUhSJ+L9AeNqa/lUw8dpGa62bFtnEN16qpTF9mE/krqPvXQQWqmdwFoOAcqcXugWNOoU0+L6tRFwmk14N7NV89u6DU1ZVatmpHR16TJOJ9j3Bcys6qZMf66iXO44qjXzUqlaGKxVMrnJOmKIH0uUnzQRKmISHFoTF1EpI8UL6mHeKjDfSPAHIFlRl/vaziHz6IfVxw+57jlHGA2jmOW0XY7cXpdx7FIyifONBZBaXGSiJ9WYzPdfHVlTD3EQx3uHTY70RDuCUTtZn6Lflxx+Jzj5m3Jcdy8zS9Or+s4Fkn5xJnGIigtThJZhb4aUw9Ruz3HaI3QhnYAVfNb9OOKw+ccs4w+ODeaBVAzd5yAx3VqaLlIyifONBZBaXGSyCqNqbfrXEd7iIdT+JzDVeruitPrOs0eQtHGgzjSeFiHHqIh4k1JvdERR3uIRT8+53DkW2ecXtdx/MvhE2cai6C0OEnEW7GSeoiHOjw2DJxsaDsZtwN+i35ccfic455tyXHcs80vTq/rOBZJ+cSZxiIoLU4S8ddqwL2br64tPmqcpGxnknTFvcNmszBbQvT13oZz+Cz6ccXhc46bt5m9GMfx4ppJUt84va7jWCTlE2cai6C0OEnEzPptolREpOCKNVGap1rlrDzgIk99JiKda/Uxvpuvtodf8lSrnJUHXOSpz0TECwoz/JKnWuWsPOAiT30mIl6KM/ySp1plV6xp1Hb7xCEihZOfpJ6nWuWsPOAiT30mIkHkJ6nnqVY5Kw+4yFOfiUgYrQbcu/naVJ16nmqVs/KAizz1mYg4oTATpSIiEmailOTlJF8geYjkpxP2k+QX4v1Pk7y4k6BFRGRznEmdZAnAPQCuAPBOAJ8k+c6Gw64AcEH8GgOwO3CcIiLiweeT+iUADpnZz83sNQBfBfDxhmM+DuD+eMjnRwDeQvKtgWMVEREHn6R+Dtbf0HU+bmv3GJAcIzlDcmZhYaHdWEVExMEnqSc9xqdxdtXnGJjZpJkNmdnQ4OCgT3wiItKGsscx81j/PJ3tAI5u4ph1Dh48+ArJpGeppWUrgFd6eP125CVWxRlWXuIE8hNrEeJMepDlKp+k/mMAF5A8H8AvAFwN4E8bjnkIwA0kvwrgfQCOmdlLrU5qZj39qE5yplVZUJbkJVbFGVZe4gTyE2s/xOlM6ma2SPIGAN8BUAJwn5k9R/K6eP8eAHsBXAngEIBTAK7dTDAiItIZn0/qMLO9iBL32rY9a743ANeHDU1ERNqVn3u/hDfZ6wDakJdYFWdYeYkTyE+shY+zZ7cJEBGR8Pr5k7qISOEoqYuIFEhfJHWSJZI/Iflwwr6dJI+RfDJ+3dqjGGdJPhPHsOH2lVm6aZpHrFnp07eQ/BrJn5F8nuRlDfsz0acecWalP9++JoYnSR4neWPDMT3vU884s9Knf0PyOZLPknyA5O807G+/P1vdl7coLwA3AfgKgIcT9u1Mau9BjLMAtrbYfyWARxGt3r0UwOMZjjUrffovAP4y/v4NAN6SxT71iDMT/dkQUwnALwFUs9inHnH2vE8R3UrlRQBvjLf/FcCfd9qfhf+kTnI7gKsAfLnXsXRIN01rA8k3A/gQgHsBwMxeM7NfNxzW8z71jDOLhgH8j5k1rgrveZ82aBZnVpQBvJFkGUAFG1fit92fhU/qAO4C8HcAllsccxnJp0g+SvLCdMLawAB8l+RBkknPtfO6aVpKXLECve/TtwFYAPBP8dDbl0me0XBMFvrUJ06g9/3Z6GoADyS0Z6FP12oWJ9DjPjWzXwD4RwCHAbyEaCX+dxsOa7s/C53USX4EwMtmdrDFYU8g+q/ZRQC+CODBNGJL8AEzuxjRvemvJ/mhhv1eN01LiSvWLPRpGcDFAHab2XsBnATQ+ICXLPSpT5xZ6M9VJN8A4GMA/i1pd0JbT96njjh73qckfxfRJ/HzAWwDcAbJP2s8LOGPtuzPQid1AB8A8DGSs4juA//HJKfWHmBmx83sRPz9XgBbSG5NO1AzOxp/fRnANxHdx36ttm+a1i2uWDPSp/MA5s3s8Xj7a4iSZ+Mxve5TZ5wZ6c+1rgDwhJn9KmFfFvp0RdM4M9KnIwBeNLMFM3sdwDcAvL/hmLb7s9BJ3cz+3sy2m1kN0X/D/sPM1v1LSPJskoy/vwRRn7yaZpwkzyD5ppXvAfwJgGcbDnsIwDXxbPil8LhpWjf4xJqFPjWzXwI4QvLtcdMwgJ82HNbzPvWJMwv92eCTaD6k0fM+XaNpnBnp08MALiVZiWMZBvB8wzFt96fXvV+KhutvRvYJAHWSiwB+A+Bqi6edU/T7AL4Zv8fKAL5iZv/ObN40zSfWLPQpAPw1gOn4v+E/B3BtRvvUFWdW+hMkKwA+DOCv1rRlrk894ux5n5rZ4yS/hmgoaBHATwBMdtqfuk2AiEiBFHr4RUSk3yipi4gUiJK6iEiBKKmLiBSIkrqISIEoqYuIFIiSuohIgfw/aEWYx2eay4kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a1 = setosa[:,3]\n",
    "a2 = setosa[:,0]\n",
    "b1 = verginica[:,3]\n",
    "b2 = verginica[:,0]\n",
    "c1 = versicolor[:,3]\n",
    "c2 = versicolor[:,0]\n",
    "d1 = unknown[:,3]\n",
    "d2 = unknown[:,0]\n",
    "\n",
    "plt.scatter(a1, a2, color='red')\n",
    "plt.scatter(b1, b2, color='blue')\n",
    "plt.scatter(c1, c2, color='green')\n",
    "plt.scatter(d1, d2, color='yellow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8.\tBuatlah pararrel coordinat plot dari data yang berkelas setosa, versicolor, dan virginica. Berikan warna merah untuk setosa, hijau untuk versicolor, biru untuk virginica. Hint gunakan perintah plot dan operasi slicing pada numpy untuk mempermudah tugas anda "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = [\"petal width\", \"petal length\", \"sepal width\", \"sepal length\"]\n",
    "\n",
    "for x in range(len(setosa)):\n",
    "    plt.plot(name, setosa[x,:], color='red')\n",
    "for x in range(len(verginica)):\n",
    "    plt.plot(name, verginica[x,:], color='blue')\n",
    "for x in range(len(versicolor)):\n",
    "    plt.plot(name, versicolor[x,:], color='green')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9.\tJelaskan bagaimana karakter pengelompokan setiap kelas pada scatterplot (parallel coordinat?)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "70e32dc76d27417f5e9ec5e494b9f807900c34e0fc91e8110dad087c5aafef2c"
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  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
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}