Iris Data Set all algorithm.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Install Packages**\n",
    "Install the packages we are going to use today. Packages are third party add-ons or libraries that we can use in R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: lattice\n",
      "Loading required package: ggplot2\n",
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 3.5.2\""
     ]
    }
   ],
   "source": [
    "# install.package(\"caret\")\n",
    "library(caret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# attach the iris dataset to the environment\n",
    "data(iris)\n",
    "# rename the dataset\n",
    "dataset <- iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of 80% of the rows in the original dataset we can use for training\n",
    "validation_index <- createDataPartition(dataset$Species, p=0.80, list=FALSE)\n",
    "# select 20% of the data for validation\n",
    "validation <- dataset[-validation_index,]\n",
    "# use the remaining 80% of data to training and testing the models\n",
    "dataset <- dataset[validation_index,]\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dimensions of dataset\n",
    "dim(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list types for each attribute\n",
    "sapply(dataset, class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take a peek at the first 5 rows of the data\n",
    "head(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take a peek at the Last 5 rows of the data\n",
    "tail(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list the levels for the class\n",
    "levels(dataset$Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize the class distribution\n",
    "percentage <- prop.table(table(dataset$Species)) * 100\n",
    "cbind(freq=table(dataset$Species), percentage=percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize attribute distributions\n",
    "summary(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split input and output\n",
    "x <- dataset[,1:4]\n",
    "y <- dataset[,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# boxplot for each attribute on one image\n",
    "par(mfrow=c(1,4))\n",
    "  for(i in 1:4) {\n",
    "  boxplot(x[,i], main=names(iris)[i])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# barplot for class breakdown\n",
    "plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# box and whisker plots for each attribute\n",
    "featurePlot(x=x, y=y, plot=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# density plots for each attribute by class value\n",
    "scales <- list(x=list(relation=\"free\"), y=list(relation=\"free\"))\n",
    "featurePlot(x=x, y=y, plot=\"density\", scales=scales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Run algorithms using 10-fold cross validation\n",
    "control <- trainControl(method=\"cv\", number=10)\n",
    "metric <- \"Accuracy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run algorithms using 10-fold cross validation\n",
    "control <- trainControl(method=\"cv\", number=10)\n",
    "metric <- \"Accuracy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a) linear algorithms\n",
    "set.seed(7)\n",
    "fit.lda <- train(Species~., data=dataset, method=\"lda\", metric=metric, trControl=control)\n",
    "# b) nonlinear algorithms\n",
    "# CART\n",
    "set.seed(7)\n",
    "fit.cart <- train(Species~., data=dataset, method=\"rpart\", metric=metric, trControl=control)\n",
    "# kNN\n",
    "set.seed(7)\n",
    "fit.knn <- train(Species~., data=dataset, method=\"knn\", metric=metric, trControl=control)\n",
    "# c) advanced algorithms\n",
    "# SVM\n",
    "set.seed(7)\n",
    "fit.svm <- train(Species~., data=dataset, method=\"svmRadial\", metric=metric, trControl=control)\n",
    "# Random Forest\n",
    "set.seed(7)\n",
    "fit.rf <- train(Species~., data=dataset, method=\"rf\", metric=metric, trControl=control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize accuracy of models\n",
    "results <- resamples(list(lda=fit.lda, cart=fit.cart, knn=fit.knn, svm=fit.svm, rf=fit.rf))\n",
    "summary(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare accuracy of models\n",
    "dotplot(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize Best Model\n",
    "print(fit.lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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