57 KNN Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Project\n",
    "\n",
    "Since KNN is such a simple algorithm, we will just use this project as a simple exercise to test your understanding of the implemetation of KNN. By now, you should feel comfortable implementing a machine learning algorithm in R, as long as you know which library to use.\n",
    "\n",
    "So for this project, just follow along with the bolded instructions. It should be very simple.\n",
    "\n",
    "## Get the Data\n",
    "\n",
    "### The iris Data Set\n",
    "\n",
    "We'll use the famous iris dataset for this porject. It's a small dataset with flower features that can be used to attempt to predict the species of an iris flower.\n",
    "\n",
    "__Use the ISLR library to get the iris dataset. Check the head and structure of the Iris dataframe.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>setosa</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n",
       "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n",
       "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n",
       "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n",
       "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n",
       "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | Sepal.Length &lt;dbl&gt; | Sepal.Width &lt;dbl&gt; | Petal.Length &lt;dbl&gt; | Petal.Width &lt;dbl&gt; | Species &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n",
       "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n",
       "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n",
       "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n",
       "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n",
       "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n",
       "\n"
      ],
      "text/plain": [
       "  Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
       "1 5.1          3.5         1.4          0.2         setosa \n",
       "2 4.9          3.0         1.4          0.2         setosa \n",
       "3 4.7          3.2         1.3          0.2         setosa \n",
       "4 4.6          3.1         1.5          0.2         setosa \n",
       "5 5.0          3.6         1.4          0.2         setosa \n",
       "6 5.4          3.9         1.7          0.4         setosa "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t150 obs. of  5 variables:\n",
      " $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...\n",
      " $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...\n",
      " $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...\n",
      " $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...\n",
      " $ Species     : Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "library(ISLR)\n",
    "head(iris)\n",
    "str(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the Data\n",
    "In this case, the iris dataset has all its features in the same order of magnitiude, but it's good practice (especially with KNN) to standardize features in your data. Let's go ahead and do this even though it's not necessary for this data!\n",
    "\n",
    "__Use scale() to standardize the feature columns of the iris dataset. Set this standardized version of the data as a new variable df__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df <- scale(iris[,1:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Check that the scaling worked by checking the variable of one of the new columns__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1"
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       "1"
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       "1"
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       "[1] 1"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var(df[,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Join the standaridized data with the resonse/target/label column (the column with the species name)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df <- cbind(df,iris[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>-0.8976739</td><td> 1.01560199</td><td>-1.335752</td><td>-1.311052</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>-1.1392005</td><td>-0.13153881</td><td>-1.335752</td><td>-1.311052</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>-1.3807271</td><td> 0.32731751</td><td>-1.392399</td><td>-1.311052</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>-1.5014904</td><td> 0.09788935</td><td>-1.279104</td><td>-1.311052</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>-1.0184372</td><td> 1.24503015</td><td>-1.335752</td><td>-1.311052</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>-0.5353840</td><td> 1.93331463</td><td>-1.165809</td><td>-1.048667</td><td>setosa</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & -0.8976739 &  1.01560199 & -1.335752 & -1.311052 & setosa\\\\\n",
       "\t2 & -1.1392005 & -0.13153881 & -1.335752 & -1.311052 & setosa\\\\\n",
       "\t3 & -1.3807271 &  0.32731751 & -1.392399 & -1.311052 & setosa\\\\\n",
       "\t4 & -1.5014904 &  0.09788935 & -1.279104 & -1.311052 & setosa\\\\\n",
       "\t5 & -1.0184372 &  1.24503015 & -1.335752 & -1.311052 & setosa\\\\\n",
       "\t6 & -0.5353840 &  1.93331463 & -1.165809 & -1.048667 & setosa\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | Sepal.Length &lt;dbl&gt; | Sepal.Width &lt;dbl&gt; | Petal.Length &lt;dbl&gt; | Petal.Width &lt;dbl&gt; | Species &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | -0.8976739 |  1.01560199 | -1.335752 | -1.311052 | setosa |\n",
       "| 2 | -1.1392005 | -0.13153881 | -1.335752 | -1.311052 | setosa |\n",
       "| 3 | -1.3807271 |  0.32731751 | -1.392399 | -1.311052 | setosa |\n",
       "| 4 | -1.5014904 |  0.09788935 | -1.279104 | -1.311052 | setosa |\n",
       "| 5 | -1.0184372 |  1.24503015 | -1.335752 | -1.311052 | setosa |\n",
       "| 6 | -0.5353840 |  1.93331463 | -1.165809 | -1.048667 | setosa |\n",
       "\n"
      ],
      "text/plain": [
       "  Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
       "1 -0.8976739    1.01560199 -1.335752    -1.311052   setosa \n",
       "2 -1.1392005   -0.13153881 -1.335752    -1.311052   setosa \n",
       "3 -1.3807271    0.32731751 -1.392399    -1.311052   setosa \n",
       "4 -1.5014904    0.09788935 -1.279104    -1.311052   setosa \n",
       "5 -1.0184372    1.24503015 -1.335752    -1.311052   setosa \n",
       "6 -0.5353840    1.93331463 -1.165809    -1.048667   setosa "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test Splits\n",
    "__Use the caTools library to split your standardized data into train and test sets. Use a 70/30 split.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "set.seed(101)\n",
    "\n",
    "library(caTools)\n",
    "\n",
    "sample <- sample.split(df$Species, SplitRatio = .70)\n",
    "train <- subset(df, sample == TRUE)\n",
    "test <- subset(df, sample == FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a KNN Model\n",
    "__Call the class library__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Use the KNN function to predict Species of the test set. Use k = 1__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted.species <- knn(train[1:4], test[1:4], train$Species, k = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>setosa</li><li>versicolor</li><li>versicolor</li><li>versicolor</li><li>versicolor</li><li>versicolor</li><li>virginica</li><li>versicolor</li><li>versicolor</li><li>versicolor</li><li>versicolor</li><li>versicolor</li><li>virginica</li><li>versicolor</li><li>versicolor</li><li>versicolor</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li><li>virginica</li></ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<style>\n",
       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
       "\t.list-inline>li {display: inline-block}\n",
       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "\t</style>\n",
       "\t<ol class=list-inline><li>'setosa'</li><li>'versicolor'</li><li>'virginica'</li></ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item setosa\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item virginica\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item virginica\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item versicolor\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\item virginica\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'setosa'\n",
       "\\item 'versicolor'\n",
       "\\item 'virginica'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. setosa\n",
       "2. setosa\n",
       "3. setosa\n",
       "4. setosa\n",
       "5. setosa\n",
       "6. setosa\n",
       "7. setosa\n",
       "8. setosa\n",
       "9. setosa\n",
       "10. setosa\n",
       "11. setosa\n",
       "12. setosa\n",
       "13. setosa\n",
       "14. setosa\n",
       "15. setosa\n",
       "16. versicolor\n",
       "17. versicolor\n",
       "18. versicolor\n",
       "19. versicolor\n",
       "20. versicolor\n",
       "21. virginica\n",
       "22. versicolor\n",
       "23. versicolor\n",
       "24. versicolor\n",
       "25. versicolor\n",
       "26. versicolor\n",
       "27. virginica\n",
       "28. versicolor\n",
       "29. versicolor\n",
       "30. versicolor\n",
       "31. virginica\n",
       "32. virginica\n",
       "33. virginica\n",
       "34. virginica\n",
       "35. virginica\n",
       "36. virginica\n",
       "37. virginica\n",
       "38. virginica\n",
       "39. virginica\n",
       "40. virginica\n",
       "41. virginica\n",
       "42. virginica\n",
       "43. virginica\n",
       "44. virginica\n",
       "45. virginica\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'setosa'\n",
       "2. 'versicolor'\n",
       "3. 'virginica'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] setosa     setosa     setosa     setosa     setosa     setosa    \n",
       " [7] setosa     setosa     setosa     setosa     setosa     setosa    \n",
       "[13] setosa     setosa     setosa     versicolor versicolor versicolor\n",
       "[19] versicolor versicolor virginica  versicolor versicolor versicolor\n",
       "[25] versicolor versicolor virginica  versicolor versicolor versicolor\n",
       "[31] virginica  virginica  virginica  virginica  virginica  virginica \n",
       "[37] virginica  virginica  virginica  virginica  virginica  virginica \n",
       "[43] virginica  virginica  virginica \n",
       "Levels: setosa versicolor virginica"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted.species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__What was your misclassification rate?__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0444444444444444"
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       "0.0444444444444444"
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      "text/plain": [
       "[1] 0.04444444"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(test$Species != predicted.species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing a good K Value\n",
    "\n",
    "Although our data is too small for us to really get a good feel for choosing a good K value, let's try anyway.\n",
    "\n",
    "__Create a plot of the error (misclassification) rate for k values ranging from 1 to 10__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dYvSV4ng/kVCnBEWj8Bx1ybX6GAIgECtuH/vcV7QO0BigQIoPNhBd5ZIi5KpNVL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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted.species <- NULL\n",
    "error.rate <- NULL\n",
    "\n",
    "for(i in 1:10){\n",
    "    set.seed(101)\n",
    "    predicted.species <- knn(train[1:4],test[1:4],train$Species,k=i)\n",
    "    error.rate[i] <- mean(test$Species != predicted.species)\n",
    "}\n",
    "\n",
    "library(ggplot2)\n",
    "k.values <- 1:10\n",
    "error.df <- data.frame(error.rate,k.values)\n",
    "pl <- ggplot(error.df,aes(x=k.values,y=error.rate)) + geom_point()\n",
    "pl + geom_line(lty=\"dotted\",color='red')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__You should have noticed that the error drops to its lowest for k values between 2-6. Then it begins to jump back up again, this is due to how small the data set it. At k=10 you begin to approach setting k=10% of the data, which is quite large.__\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}