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Author: jaydipkumar

LDA.ipynb

@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "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)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}