Fruit_Classification_with_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fruit Classification with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a simple dataset for training a classifier to distinguish between different types of fruits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries and load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>80</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>76</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>178</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79\n",
       "5            2   mandarin      mandarin    80    5.8     4.3         0.77\n",
       "6            2   mandarin      mandarin    80    5.9     4.3         0.81\n",
       "7            2   mandarin      mandarin    76    5.8     4.0         0.81\n",
       "8            1      apple      braeburn   178    7.1     7.8         0.92\n",
       "9            1      apple      braeburn   172    7.4     7.0         0.89"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = pd.read_table('fruit_data_with_colors.txt')\n",
    "fruits.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['apple', 'mandarin', 'orange', 'lemon'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits['fruit_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(59, 7)\n"
     ]
    }
   ],
   "source": [
    "print(fruits.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.542373</td>\n",
       "      <td>163.118644</td>\n",
       "      <td>7.105085</td>\n",
       "      <td>7.693220</td>\n",
       "      <td>0.762881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.208048</td>\n",
       "      <td>55.018832</td>\n",
       "      <td>0.816938</td>\n",
       "      <td>1.361017</td>\n",
       "      <td>0.076857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.930000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fruit_label        mass      width     height  color_score\n",
       "count    59.000000   59.000000  59.000000  59.000000    59.000000\n",
       "mean      2.542373  163.118644   7.105085   7.693220     0.762881\n",
       "std       1.208048   55.018832   0.816938   1.361017     0.076857\n",
       "min       1.000000   76.000000   5.800000   4.000000     0.550000\n",
       "25%       1.000000  140.000000   6.600000   7.200000     0.720000\n",
       "50%       3.000000  158.000000   7.200000   7.600000     0.750000\n",
       "75%       4.000000  177.000000   7.500000   8.200000     0.810000\n",
       "max       4.000000  362.000000   9.600000  10.500000     0.930000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fruit type distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fruit_name\n",
      "apple       19\n",
      "lemon       16\n",
      "mandarin     5\n",
      "orange      19\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(fruits.groupby('fruit_name').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(data=fruits,x='fruit_name',label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is pretty balanced except mandarin. We will just have to go with it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as pl\n",
    "fruits.drop('fruit_label' ,axis=1).hist(bins=30, figsize=(9,9))\n",
    "pl.suptitle(\"Histogram for each numeric input variable\")\n",
    "plt.savefig('fruits_hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    mass  width  height  color_score\n",
      "0    192    8.4     7.3         0.55\n",
      "1    180    8.0     6.8         0.59\n",
      "2    176    7.4     7.2         0.60\n",
      "3     86    6.2     4.7         0.80\n",
      "4     84    6.0     4.6         0.79\n",
      "5     80    5.8     4.3         0.77\n",
      "6     80    5.9     4.3         0.81\n",
      "7     76    5.8     4.0         0.81\n",
      "8    178    7.1     7.8         0.92\n",
      "9    172    7.4     7.0         0.89\n",
      "10   166    6.9     7.3         0.93\n",
      "11   172    7.1     7.6         0.92\n",
      "12   154    7.0     7.1         0.88\n",
      "13   164    7.3     7.7         0.70\n",
      "14   152    7.6     7.3         0.69\n",
      "15   156    7.7     7.1         0.69\n",
      "16   156    7.6     7.5         0.67\n",
      "17   168    7.5     7.6         0.73\n",
      "18   162    7.5     7.1         0.83\n",
      "19   162    7.4     7.2         0.85\n",
      "20   160    7.5     7.5         0.86\n",
      "21   156    7.4     7.4         0.84\n",
      "22   140    7.3     7.1         0.87\n",
      "23   170    7.6     7.9         0.88\n",
      "24   342    9.0     9.4         0.75\n",
      "25   356    9.2     9.2         0.75\n",
      "26   362    9.6     9.2         0.74\n",
      "27   204    7.5     9.2         0.77\n",
      "28   140    6.7     7.1         0.72\n",
      "29   160    7.0     7.4         0.81\n",
      "30   158    7.1     7.5         0.79\n",
      "31   210    7.8     8.0         0.82\n",
      "32   164    7.2     7.0         0.80\n",
      "33   190    7.5     8.1         0.74\n",
      "34   142    7.6     7.8         0.75\n",
      "35   150    7.1     7.9         0.75\n",
      "36   160    7.1     7.6         0.76\n",
      "37   154    7.3     7.3         0.79\n",
      "38   158    7.2     7.8         0.77\n",
      "39   144    6.8     7.4         0.75\n",
      "40   154    7.1     7.5         0.78\n",
      "41   180    7.6     8.2         0.79\n",
      "42   154    7.2     7.2         0.82\n",
      "43   194    7.2    10.3         0.70\n",
      "44   200    7.3    10.5         0.72\n",
      "45   186    7.2     9.2         0.72\n",
      "46   216    7.3    10.2         0.71\n",
      "47   196    7.3     9.7         0.72\n",
      "48   174    7.3    10.1         0.72\n",
      "49   132    5.8     8.7         0.73\n",
      "50   130    6.0     8.2         0.71\n",
      "51   116    6.0     7.5         0.72\n",
      "52   118    5.9     8.0         0.72\n",
      "53   120    6.0     8.4         0.74\n",
      "54   116    6.1     8.5         0.71\n",
      "55   116    6.3     7.7         0.72\n",
      "56   116    5.9     8.1         0.73\n",
      "57   152    6.5     8.5         0.72\n",
      "58   118    6.1     8.1         0.70\n",
      "0     1\n",
      "1     1\n",
      "2     1\n",
      "3     2\n",
      "4     2\n",
      "5     2\n",
      "6     2\n",
      "7     2\n",
      "8     1\n",
      "9     1\n",
      "10    1\n",
      "11    1\n",
      "12    1\n",
      "13    1\n",
      "14    1\n",
      "15    1\n",
      "16    1\n",
      "17    1\n",
      "18    1\n",
      "19    1\n",
      "20    1\n",
      "21    1\n",
      "22    1\n",
      "23    1\n",
      "24    3\n",
      "25    3\n",
      "26    3\n",
      "27    3\n",
      "28    3\n",
      "29    3\n",
      "30    3\n",
      "31    3\n",
      "32    3\n",
      "33    3\n",
      "34    3\n",
      "35    3\n",
      "36    3\n",
      "37    3\n",
      "38    3\n",
      "39    3\n",
      "40    3\n",
      "41    3\n",
      "42    3\n",
      "43    4\n",
      "44    4\n",
      "45    4\n",
      "46    4\n",
      "47    4\n",
      "48    4\n",
      "49    4\n",
      "50    4\n",
      "51    4\n",
      "52    4\n",
      "53    4\n",
      "54    4\n",
      "55    4\n",
      "56    4\n",
      "57    4\n",
      "58    4\n",
      "Name: fruit_label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "feature_names = ['mass', 'width', 'height', 'color_score']\n",
    "X = fruits[feature_names]\n",
    "y = fruits['fruit_label']\n",
    "print(X)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    mass  width  height  color_score\n",
      "42   154    7.2     7.2         0.82\n",
      "48   174    7.3    10.1         0.72\n",
      "7     76    5.8     4.0         0.81\n",
      "14   152    7.6     7.3         0.69\n",
      "32   164    7.2     7.0         0.80\n",
      "49   132    5.8     8.7         0.73\n",
      "29   160    7.0     7.4         0.81\n",
      "37   154    7.3     7.3         0.79\n",
      "56   116    5.9     8.1         0.73\n",
      "18   162    7.5     7.1         0.83\n",
      "55   116    6.3     7.7         0.72\n",
      "27   204    7.5     9.2         0.77\n",
      "15   156    7.7     7.1         0.69\n",
      "5     80    5.8     4.3         0.77\n",
      "31   210    7.8     8.0         0.82\n",
      "16   156    7.6     7.5         0.67\n",
      "50   130    6.0     8.2         0.71\n",
      "20   160    7.5     7.5         0.86\n",
      "51   116    6.0     7.5         0.72\n",
      "8    178    7.1     7.8         0.92\n",
      "13   164    7.3     7.7         0.70\n",
      "25   356    9.2     9.2         0.75\n",
      "17   168    7.5     7.6         0.73\n",
      "58   118    6.1     8.1         0.70\n",
      "57   152    6.5     8.5         0.72\n",
      "52   118    5.9     8.0         0.72\n",
      "38   158    7.2     7.8         0.77\n",
      "1    180    8.0     6.8         0.59\n",
      "12   154    7.0     7.1         0.88\n",
      "45   186    7.2     9.2         0.72\n",
      "24   342    9.0     9.4         0.75\n",
      "6     80    5.9     4.3         0.81\n",
      "23   170    7.6     7.9         0.88\n",
      "36   160    7.1     7.6         0.76\n",
      "21   156    7.4     7.4         0.84\n",
      "19   162    7.4     7.2         0.85\n",
      "9    172    7.4     7.0         0.89\n",
      "39   144    6.8     7.4         0.75\n",
      "54   116    6.1     8.5         0.71\n",
      "3     86    6.2     4.7         0.80\n",
      "0    192    8.4     7.3         0.55\n",
      "53   120    6.0     8.4         0.74\n",
      "47   196    7.3     9.7         0.72\n",
      "44   200    7.3    10.5         0.72     mass  width  height  color_score\n",
      "26   362    9.6     9.2         0.74\n",
      "35   150    7.1     7.9         0.75\n",
      "43   194    7.2    10.3         0.70\n",
      "28   140    6.7     7.1         0.72\n",
      "11   172    7.1     7.6         0.92\n",
      "2    176    7.4     7.2         0.60\n",
      "34   142    7.6     7.8         0.75\n",
      "46   216    7.3    10.2         0.71\n",
      "40   154    7.1     7.5         0.78\n",
      "22   140    7.3     7.1         0.87\n",
      "4     84    6.0     4.6         0.79\n",
      "10   166    6.9     7.3         0.93\n",
      "30   158    7.1     7.5         0.79\n",
      "41   180    7.6     8.2         0.79\n",
      "33   190    7.5     8.1         0.74 42    3\n",
      "48    4\n",
      "7     2\n",
      "14    1\n",
      "32    3\n",
      "49    4\n",
      "29    3\n",
      "37    3\n",
      "56    4\n",
      "18    1\n",
      "55    4\n",
      "27    3\n",
      "15    1\n",
      "5     2\n",
      "31    3\n",
      "16    1\n",
      "50    4\n",
      "20    1\n",
      "51    4\n",
      "8     1\n",
      "13    1\n",
      "25    3\n",
      "17    1\n",
      "58    4\n",
      "57    4\n",
      "52    4\n",
      "38    3\n",
      "1     1\n",
      "12    1\n",
      "45    4\n",
      "24    3\n",
      "6     2\n",
      "23    1\n",
      "36    3\n",
      "21    1\n",
      "19    1\n",
      "9     1\n",
      "39    3\n",
      "54    4\n",
      "3     2\n",
      "0     1\n",
      "53    4\n",
      "47    4\n",
      "44    4\n",
      "Name: fruit_label, dtype: int64 26    3\n",
      "35    3\n",
      "43    4\n",
      "28    3\n",
      "11    1\n",
      "2     1\n",
      "34    3\n",
      "46    4\n",
      "40    3\n",
      "22    1\n",
      "4     2\n",
      "10    1\n",
      "30    3\n",
      "41    3\n",
      "33    3\n",
      "Name: fruit_label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "print(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply scalling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.27857143 0.41176471 0.49230769 0.72972973]\n",
      " [0.35       0.44117647 0.93846154 0.45945946]\n",
      " [0.         0.         0.         0.7027027 ]\n",
      " [0.27142857 0.52941176 0.50769231 0.37837838]\n",
      " [0.31428571 0.41176471 0.46153846 0.67567568]\n",
      " [0.2        0.         0.72307692 0.48648649]\n",
      " [0.3        0.35294118 0.52307692 0.7027027 ]\n",
      " [0.27857143 0.44117647 0.50769231 0.64864865]\n",
      " [0.14285714 0.02941176 0.63076923 0.48648649]\n",
      " [0.30714286 0.5        0.47692308 0.75675676]\n",
      " [0.14285714 0.14705882 0.56923077 0.45945946]\n",
      " [0.45714286 0.5        0.8        0.59459459]\n",
      " [0.28571429 0.55882353 0.47692308 0.37837838]\n",
      " [0.01428571 0.         0.04615385 0.59459459]\n",
      " [0.47857143 0.58823529 0.61538462 0.72972973]\n",
      " [0.28571429 0.52941176 0.53846154 0.32432432]\n",
      " [0.19285714 0.05882353 0.64615385 0.43243243]\n",
      " [0.3        0.5        0.53846154 0.83783784]\n",
      " [0.14285714 0.05882353 0.53846154 0.45945946]\n",
      " [0.36428571 0.38235294 0.58461538 1.        ]\n",
      " [0.31428571 0.44117647 0.56923077 0.40540541]\n",
      " [1.         1.         0.8        0.54054054]\n",
      " [0.32857143 0.5        0.55384615 0.48648649]\n",
      " [0.15       0.08823529 0.63076923 0.40540541]\n",
      " [0.27142857 0.20588235 0.69230769 0.45945946]\n",
      " [0.15       0.02941176 0.61538462 0.45945946]\n",
      " [0.29285714 0.41176471 0.58461538 0.59459459]\n",
      " [0.37142857 0.64705882 0.43076923 0.10810811]\n",
      " [0.27857143 0.35294118 0.47692308 0.89189189]\n",
      " [0.39285714 0.41176471 0.8        0.45945946]\n",
      " [0.95       0.94117647 0.83076923 0.54054054]\n",
      " [0.01428571 0.02941176 0.04615385 0.7027027 ]\n",
      " [0.33571429 0.52941176 0.6        0.89189189]\n",
      " [0.3        0.38235294 0.55384615 0.56756757]\n",
      " [0.28571429 0.47058824 0.52307692 0.78378378]\n",
      " [0.30714286 0.47058824 0.49230769 0.81081081]\n",
      " [0.34285714 0.47058824 0.46153846 0.91891892]\n",
      " [0.24285714 0.29411765 0.52307692 0.54054054]\n",
      " [0.14285714 0.08823529 0.69230769 0.43243243]\n",
      " [0.03571429 0.11764706 0.10769231 0.67567568]\n",
      " [0.41428571 0.76470588 0.50769231 0.        ]\n",
      " [0.15714286 0.05882353 0.67692308 0.51351351]\n",
      " [0.42857143 0.44117647 0.87692308 0.45945946]\n",
      " [0.44285714 0.44117647 1.         0.45945946]]\n",
      "[[1.02142857 1.11764706 0.8        0.51351351]\n",
      " [0.26428571 0.38235294 0.6        0.54054054]\n",
      " [0.42142857 0.41176471 0.96923077 0.40540541]\n",
      " [0.22857143 0.26470588 0.47692308 0.45945946]\n",
      " [0.34285714 0.38235294 0.55384615 1.        ]\n",
      " [0.35714286 0.47058824 0.49230769 0.13513514]\n",
      " [0.23571429 0.52941176 0.58461538 0.54054054]\n",
      " [0.5        0.44117647 0.95384615 0.43243243]\n",
      " [0.27857143 0.38235294 0.53846154 0.62162162]\n",
      " [0.22857143 0.44117647 0.47692308 0.86486486]\n",
      " [0.02857143 0.05882353 0.09230769 0.64864865]\n",
      " [0.32142857 0.32352941 0.50769231 1.02702703]\n",
      " [0.29285714 0.38235294 0.53846154 0.64864865]\n",
      " [0.37142857 0.52941176 0.64615385 0.64864865]\n",
      " [0.40714286 0.5        0.63076923 0.51351351]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "print(X_train)\n",
    "print(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Models\n",
    "\n",
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression classifier on training set: 0.75\n",
      "Accuracy of Logistic regression classifier on test set: 0.47\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Logistic regression classifier on training set: {:.2f}'\n",
    "     .format(logreg.score(X_train, y_train)))\n",
    "print('Accuracy of Logistic regression classifier on test set: {:.2f}'\n",
    "     .format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 1.00\n",
      "Accuracy of Decision Tree classifier on test set: 0.87\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(clf.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting max decision tree depth to help avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 0.89\n",
      "Accuracy of Decision Tree classifier on test set: 0.73\n"
     ]
    }
   ],
   "source": [
    "clf2 = DecisionTreeClassifier(max_depth=3).fit(X_train, y_train)\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(clf2.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(clf2.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier on training set: 0.91\n",
      "Accuracy of SVM classifier on test set: 0.80\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "print('Accuracy of SVM classifier on training set: {:.2f}'\n",
    "     .format(svm.score(X_train, y_train)))\n",
    "print('Accuracy of SVM classifier on test set: {:.2f}'\n",
    "     .format(svm.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 0.95\n",
      "Accuracy of K-NN classifier on test set: 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f087cfec5e0>,\n",
       "  <matplotlib.axis.XTick at 0x7f087cf08310>,\n",
       "  <matplotlib.axis.XTick at 0x7f087cff0e50>,\n",
       "  <matplotlib.axis.XTick at 0x7f087cf26a00>,\n",
       "  <matplotlib.axis.XTick at 0x7f087cf26d30>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_range = range(1, 20)\n",
    "scores = []\n",
    "\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors = k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    scores.append(knn.score(X_test, y_test))\n",
    "plt.figure()\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('accuracy')\n",
    "plt.scatter(k_range, scores)\n",
    "plt.xticks([0,5,10,15,20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this particular dateset, we obtain the highest accuracy when k=5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}