LDA.ipynb

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   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "#import Library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data using url\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n",
    "dataset = pd.read_csv(url, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X = dataset.iloc[:, 0:4].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spilt data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scalling data\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LDA Model\n",
    "lda = LDA(n_components=1)\n",
    "X_train = lda.fit_transform(X_train, y_train)\n",
    "X_test = lda.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rendom Forest classifier\n",
    "classifier = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  0  0]\n",
      " [ 0 13  0]\n",
      " [ 0  0  6]]\n",
      "Accuracy1.0\n"
     ]
    }
   ],
   "source": [
    "#confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print('Accuracy' + str(accuracy_score(y_test, y_pred)))"
   ]
  }
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