K-Means on Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nMetaData about the dataset\\n\\nPclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\\nsurvival Survival (0 = No; 1 = Yes)\\nname Name\\nsex Sex\\nage Age\\nsibsp Number of Siblings/Spouses Aboard\\nparch Number of Parents/Children Aboard\\nticket Ticket Number\\nfare Passenger Fare (British pound)\\ncabin Cabin\\nembarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\\nboat Lifeboat\\nbody Body Identification Number\\nhome.dest Home/Destination\\n'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "MetaData about the dataset\n",
    "\n",
    "Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "survival Survival (0 = No; 1 = Yes)\n",
    "name Name\n",
    "sex Sex\n",
    "age Age\n",
    "sibsp Number of Siblings/Spouses Aboard\n",
    "parch Number of Parents/Children Aboard\n",
    "ticket Ticket Number\n",
    "fare Passenger Fare (British pound)\n",
    "cabin Cabin\n",
    "embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "boat Lifeboat\n",
    "body Body Identification Number\n",
    "home.dest Home/Destination\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('titanic.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "      <th>home.dest</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   pclass  survived     sex      age  sibsp  parch  ticket      fare    cabin  \\\n",
       "0       1         1  female  29.0000      0      0   24160  211.3375       B5   \n",
       "1       1         1    male   0.9167      1      2  113781  151.5500  C22 C26   \n",
       "2       1         0  female   2.0000      1      2  113781  151.5500  C22 C26   \n",
       "3       1         0    male  30.0000      1      2  113781  151.5500  C22 C26   \n",
       "4       1         0  female  25.0000      1      2  113781  151.5500  C22 C26   \n",
       "\n",
       "  embarked boat                        home.dest  \n",
       "0        S    2                     St Louis, MO  \n",
       "1        S   11  Montreal, PQ / Chesterville, ON  \n",
       "2        S  NaN  Montreal, PQ / Chesterville, ON  \n",
       "3        S  NaN  Montreal, PQ / Chesterville, ON  \n",
       "4        S  NaN  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dealing with Missing data\n",
    "df.drop(['body','name'],1,inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nimish/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
      "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  \n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
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       "      <td>St Louis, MO</td>\n",
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       "      <td>1</td>\n",
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       "      <td>C22 C26</td>\n",
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       "      <td>11</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "   pclass  survived     sex      age  sibsp  parch  ticket      fare    cabin  \\\n",
       "0       1         1  female  29.0000      0      0   24160  211.3375       B5   \n",
       "1       1         1    male   0.9167      1      2  113781  151.5500  C22 C26   \n",
       "2       1         0  female   2.0000      1      2  113781  151.5500  C22 C26   \n",
       "3       1         0    male  30.0000      1      2  113781  151.5500  C22 C26   \n",
       "4       1         0  female  25.0000      1      2  113781  151.5500  C22 C26   \n",
       "\n",
       "  embarked boat                        home.dest  \n",
       "0        S    2                     St Louis, MO  \n",
       "1        S   11  Montreal, PQ / Chesterville, ON  \n",
       "2        S    0  Montreal, PQ / Chesterville, ON  \n",
       "3        S    0  Montreal, PQ / Chesterville, ON  \n",
       "4        S    0  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting String values to Numbers\n",
    "df.convert_objects(convert_numeric=True)\n",
    "df.fillna(0,inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      pclass  survived  sex      age  sibsp  parch  ticket      fare  cabin  \\\n",
      "0          1         1    1  29.0000      0      0     740  211.3375     96   \n",
      "1          1         1    0   0.9167      1      2     513  151.5500     93   \n",
      "2          1         0    1   2.0000      1      2     513  151.5500     93   \n",
      "3          1         0    0  30.0000      1      2     513  151.5500     93   \n",
      "4          1         0    1  25.0000      1      2     513  151.5500     93   \n",
      "5          1         1    0  48.0000      0      0     688   26.5500     40   \n",
      "6          1         1    1  63.0000      1      0     543   77.9583    144   \n",
      "7          1         0    0  39.0000      0      0     655    0.0000     87   \n",
      "8          1         1    1  53.0000      2      0     695   51.4792     21   \n",
      "9          1         0    0  71.0000      0      0     757   49.5042      0   \n",
      "10         1         0    0  47.0000      1      0     668  227.5250    170   \n",
      "11         1         1    1  18.0000      1      0     668  227.5250    170   \n",
      "12         1         1    1  24.0000      0      0     277   69.3000     83   \n",
      "13         1         1    1  26.0000      0      0     646   78.8500      0   \n",
      "14         1         1    0  80.0000      0      0     211   30.0000    141   \n",
      "15         1         0    0   0.0000      0      0     265   25.9250      0   \n",
      "16         1         0    0  24.0000      0      1     345  247.5208     64   \n",
      "17         1         1    1  50.0000      0      1     345  247.5208     64   \n",
      "18         1         1    1  32.0000      0      0     713   76.2917     29   \n",
      "19         1         0    0  36.0000      0      0     361   75.2417     54   \n",
      "20         1         1    0  37.0000      1      1     680   52.5542    175   \n",
      "21         1         1    1  47.0000      1      1     680   52.5542    175   \n",
      "22         1         1    0  26.0000      0      0     368   30.0000     63   \n",
      "23         1         1    1  42.0000      0      0     668  227.5250      0   \n",
      "24         1         1    1  29.0000      0      0     788  221.7792    165   \n",
      "25         1         0    0  25.0000      0      0     733   26.0000      0   \n",
      "26         1         1    0  25.0000      1      0     778   91.0792    110   \n",
      "27         1         1    1  19.0000      1      0     778   91.0792    110   \n",
      "28         1         1    1  35.0000      0      0     343  135.6333     65   \n",
      "29         1         1    0  28.0000      0      0     921   26.5500     19   \n",
      "...      ...       ...  ...      ...    ...    ...     ...       ...    ...   \n",
      "1279       3         0    1  14.0000      0      0     629    7.8542      0   \n",
      "1280       3         0    0  22.0000      0      0     756    7.8958      0   \n",
      "1281       3         0    0  22.0000      0      0     408    9.0000      0   \n",
      "1282       3         0    0   0.0000      0      0     342    8.0500      0   \n",
      "1283       3         0    0   0.0000      0      0      64    7.5500      0   \n",
      "1284       3         0    0   0.0000      0      0      81    8.0500      0   \n",
      "1285       3         0    0  32.5000      0      0     316    9.5000      0   \n",
      "1286       3         1    1  38.0000      0      0     434    7.2292      0   \n",
      "1287       3         0    0  51.0000      0      0      74    7.7500      0   \n",
      "1288       3         0    0  18.0000      1      0     691    6.4958      0   \n",
      "1289       3         0    0  21.0000      1      0     639    6.4958      0   \n",
      "1290       3         1    1  47.0000      1      0     916    7.0000      0   \n",
      "1291       3         0    0   0.0000      0      0     456    8.7125      0   \n",
      "1292       3         0    0   0.0000      0      0     183    7.5500      0   \n",
      "1293       3         0    0   0.0000      0      0     278    8.0500      0   \n",
      "1294       3         0    0  28.5000      0      0     610   16.1000      0   \n",
      "1295       3         0    0  21.0000      0      0     618    7.2500      0   \n",
      "1296       3         0    0  27.0000      0      0     114    8.6625      0   \n",
      "1297       3         0    0   0.0000      0      0     763    7.2500      0   \n",
      "1298       3         0    0  36.0000      0      0     252    9.5000      0   \n",
      "1299       3         0    0  27.0000      1      0     881   14.4542      0   \n",
      "1300       3         1    1  15.0000      1      0     881   14.4542      0   \n",
      "1301       3         0    0  45.5000      0      0     903    7.2250      0   \n",
      "1302       3         0    0   0.0000      0      0     929    7.2250      0   \n",
      "1303       3         0    0   0.0000      0      0     484   14.4583      0   \n",
      "1304       3         0    1  14.5000      1      0     384   14.4542      0   \n",
      "1305       3         0    1   0.0000      1      0     384   14.4542      0   \n",
      "1306       3         0    0  26.5000      0      0      62    7.2250      0   \n",
      "1307       3         0    0  27.0000      0      0     873    7.2250      0   \n",
      "1308       3         0    0  29.0000      0      0     885    7.8750      0   \n",
      "\n",
      "      embarked  boat  home.dest  \n",
      "0            3     1         15  \n",
      "1            3    12        152  \n",
      "2            3     0        152  \n",
      "3            3     0        152  \n",
      "4            3     0        152  \n",
      "5            3    11        296  \n",
      "6            3    16        337  \n",
      "7            3     0         82  \n",
      "8            3     9        252  \n",
      "9            2     0        289  \n",
      "10           2     0        296  \n",
      "11           2     6        296  \n",
      "12           2    27         49  \n",
      "13           3    25          0  \n",
      "14           3    17        175  \n",
      "15           3     0        296  \n",
      "16           2     0        349  \n",
      "17           2    25        349  \n",
      "18           2    18          0  \n",
      "19           2    20        219  \n",
      "20           3     3        296  \n",
      "21           3     3        296  \n",
      "22           2     3        296  \n",
      "23           2     6          0  \n",
      "24           3    18          0  \n",
      "25           2     0        200  \n",
      "26           2     2        117  \n",
      "27           2     2        117  \n",
      "28           3    18          0  \n",
      "29           3     9        205  \n",
      "...        ...   ...        ...  \n",
      "1279         3     0          0  \n",
      "1280         3     0          0  \n",
      "1281         3     0        314  \n",
      "1282         3     0          0  \n",
      "1283         3     0          0  \n",
      "1284         3     0          0  \n",
      "1285         3     0          0  \n",
      "1286         2     4          0  \n",
      "1287         3     0          0  \n",
      "1288         3     0          0  \n",
      "1289         3     0          0  \n",
      "1290         3     0          0  \n",
      "1291         3     0          0  \n",
      "1292         3     0          0  \n",
      "1293         3     0          0  \n",
      "1294         3     0          0  \n",
      "1295         3     0          0  \n",
      "1296         3     0          0  \n",
      "1297         3     0          0  \n",
      "1298         3     0          0  \n",
      "1299         2     4          0  \n",
      "1300         2     0          0  \n",
      "1301         2     0          0  \n",
      "1302         2     0          0  \n",
      "1303         2     0          0  \n",
      "1304         2     0          0  \n",
      "1305         2     0          0  \n",
      "1306         2     0          0  \n",
      "1307         2     0          0  \n",
      "1308         3     0          0  \n",
      "\n",
      "[1309 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "def handle_non_numerical_data(df):\n",
    "    columns = df.columns.values\n",
    "    for column in columns:\n",
    "        text_digit_vals = {}\n",
    "        def convert_to_int(val):\n",
    "            return text_digit_vals[val]\n",
    "\n",
    "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
    "            column_contents = df[column].values.tolist()\n",
    "            unique_elements = set(column_contents)\n",
    "            x = 0\n",
    "            for unique in unique_elements:\n",
    "                if unique not in text_digit_vals:\n",
    "                    text_digit_vals[unique] = x\n",
    "                    x+=1\n",
    "\n",
    "            df[column] = list(map(convert_to_int, df[column]))\n",
    "\n",
    "    return df\n",
    "df = handle_non_numerical_data(df)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = KMeans()\n",
    "#clf.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(['survived'],1).astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['ticket'],1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1925133689839572\n",
      "0.1925133689839572\n",
      "0.1925133689839572\n",
      "0.1925133689839572\n",
      "0.1925133689839572\n"
     ]
    }
   ],
   "source": [
    "y = np.array(df['survived'])\n",
    "X = preprocessing.scale(X)\n",
    "\n",
    "def pred(times):\n",
    "    clf.fit(X)    \n",
    "    correct = 0\n",
    "    for i in range(0,times):\n",
    "        for i in range(len(X)):\n",
    "            predict_me = np.array(X[i].astype(float))\n",
    "            predict_me = predict_me.reshape(-1,len(predict_me))\n",
    "            prediction = clf.predict(predict_me)\n",
    "            if prediction[0] == y[i]:\n",
    "                correct+=1\n",
    "        print(correct/len(X))\n",
    "        correct = 0\n",
    "pred(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'correct' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-83d8828cd470>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpred\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-45-ceede6d378e6>\u001b[0m in \u001b[0;36mpred\u001b[0;34m(times)\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredict_me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                 \u001b[0mcorrect\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrect\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'correct' referenced before assignment"
     ]
    }
   ],
   "source": []
  }
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