08-10-2021(Logistic Regression).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f03c8ce3",
   "metadata": {},
   "source": [
    "### Classification\n",
    "- binary---->0,1  yes,no   pass/fail\n",
    "- multi class---->0,1,2,3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c227ce",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "- uses sigmoid function which lies b/w 0 and 1\n",
    "- sigmoid function y=1/(1+e^-z)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f0a532e2",
   "metadata": {},
   "source": [
    "1.Get the data\n",
    "2.Data prepocessing/Data exploration\n",
    "3.split the data for training and testing\n",
    "4.select the algorithm\n",
    "5.build the model\n",
    "6.evalaute the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cdfbf05f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/Datasets/master/Classification/Heart_disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4828515",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Thal</th>\n",
       "      <th>AHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>typical</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fixed</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Age  Sex     ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  \\\n",
       "0           1   63    1       typical     145   233    1        2    150   \n",
       "1           2   67    1  asymptomatic     160   286    0        2    108   \n",
       "2           3   67    1  asymptomatic     120   229    0        2    129   \n",
       "3           4   37    1    nonanginal     130   250    0        0    187   \n",
       "4           5   41    0    nontypical     130   204    0        2    172   \n",
       "\n",
       "   ExAng  Oldpeak  Slope   Ca        Thal  AHD  \n",
       "0      0      2.3      3  0.0       fixed   No  \n",
       "1      1      1.5      2  3.0      normal  Yes  \n",
       "2      1      2.6      2  2.0  reversable  Yes  \n",
       "3      0      3.5      3  0.0      normal   No  \n",
       "4      0      1.4      1  0.0      normal   No  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3b0ea238",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "Age           0\n",
       "Sex           0\n",
       "ChestPain     0\n",
       "RestBP        0\n",
       "Chol          0\n",
       "Fbs           0\n",
       "RestECG       0\n",
       "MaxHR         0\n",
       "ExAng         0\n",
       "Oldpeak       0\n",
       "Slope         0\n",
       "Ca            4\n",
       "Thal          2\n",
       "AHD           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc2ccf3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fixed', 'normal', 'reversable', nan], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Thal\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "39dce075",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df[\"Thal\"].replace(np.nan,\"fixed\",inplace=True)\n",
    "df[\"Ca\"].replace(np.nan,0.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f9197437",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "Age           0\n",
       "Sex           0\n",
       "ChestPain     0\n",
       "RestBP        0\n",
       "Chol          0\n",
       "Fbs           0\n",
       "RestECG       0\n",
       "MaxHR         0\n",
       "ExAng         0\n",
       "Oldpeak       0\n",
       "Slope         0\n",
       "Ca            0\n",
       "Thal          0\n",
       "AHD           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d7328f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ChestPain\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ec8e093c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label=LabelEncoder()\n",
    "df[\"ChestPain\"]=label.fit_transform(df[\"ChestPain\"])\n",
    "df[\"Thal\"]=label.fit_transform(df[\"Thal\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fcad4795",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
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       "      <td>130</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
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       "      <td>110</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>68</td>\n",
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       "      <td>144</td>\n",
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       "      <td>3.4</td>\n",
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       "      <td>Yes</td>\n",
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       "    <tr>\n",
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       "      <td>303</td>\n",
       "      <td>38</td>\n",
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       "      <td>138</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
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       "</table>\n",
       "<p>303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Age  Sex  ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  \\\n",
       "0             1   63    1          3     145   233    1        2    150   \n",
       "1             2   67    1          0     160   286    0        2    108   \n",
       "2             3   67    1          0     120   229    0        2    129   \n",
       "3             4   37    1          1     130   250    0        0    187   \n",
       "4             5   41    0          2     130   204    0        2    172   \n",
       "..          ...  ...  ...        ...     ...   ...  ...      ...    ...   \n",
       "298         299   45    1          3     110   264    0        0    132   \n",
       "299         300   68    1          0     144   193    1        0    141   \n",
       "300         301   57    1          0     130   131    0        0    115   \n",
       "301         302   57    0          2     130   236    0        2    174   \n",
       "302         303   38    1          1     138   175    0        0    173   \n",
       "\n",
       "     ExAng  Oldpeak  Slope   Ca  Thal  AHD  \n",
       "0        0      2.3      3  0.0     0   No  \n",
       "1        1      1.5      2  3.0     1  Yes  \n",
       "2        1      2.6      2  2.0     2  Yes  \n",
       "3        0      3.5      3  0.0     1   No  \n",
       "4        0      1.4      1  0.0     1   No  \n",
       "..     ...      ...    ...  ...   ...  ...  \n",
       "298      0      1.2      2  0.0     2  Yes  \n",
       "299      0      3.4      2  2.0     2  Yes  \n",
       "300      1      1.2      2  1.0     2  Yes  \n",
       "301      0      0.0      2  1.0     1  Yes  \n",
       "302      0      0.0      1  0.0     1   No  \n",
       "\n",
       "[303 rows x 15 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e6e0adfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fixed', 'normal', 'reversable'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Thal\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b30eb1d1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Age  Sex  ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  \\\n",
       "0     63    1          3     145   233    1        2    150      0      2.3   \n",
       "1     67    1          0     160   286    0        2    108      1      1.5   \n",
       "2     67    1          0     120   229    0        2    129      1      2.6   \n",
       "3     37    1          1     130   250    0        0    187      0      3.5   \n",
       "4     41    0          2     130   204    0        2    172      0      1.4   \n",
       "..   ...  ...        ...     ...   ...  ...      ...    ...    ...      ...   \n",
       "298   45    1          3     110   264    0        0    132      0      1.2   \n",
       "299   68    1          0     144   193    1        0    141      0      3.4   \n",
       "300   57    1          0     130   131    0        0    115      1      1.2   \n",
       "301   57    0          2     130   236    0        2    174      0      0.0   \n",
       "302   38    1          1     138   175    0        0    173      0      0.0   \n",
       "\n",
       "     Slope   Ca  Thal  \n",
       "0        3  0.0     0  \n",
       "1        2  3.0     1  \n",
       "2        2  2.0     2  \n",
       "3        3  0.0     1  \n",
       "4        1  0.0     1  \n",
       "..     ...  ...   ...  \n",
       "298      2  0.0     2  \n",
       "299      2  2.0     2  \n",
       "300      2  1.0     2  \n",
       "301      2  1.0     1  \n",
       "302      1  0.0     1  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.drop([\"AHD\",\"Unnamed: 0\"],axis=1)  #inputs/features\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4d2623fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df[\"AHD\"]  #output/target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a8d0062c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.8,random_state=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2a40a492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 13)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "07e7e2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 15)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "eeb84f31",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  \\\n",
       "1     67    1          0     160   286    0        2    108      1      1.5   \n",
       "142   52    1          2     128   205    1        0    184      0      0.0   \n",
       "126   56    0          0     200   288    1        2    133      1      4.0   \n",
       "37    57    1          0     150   276    0        2    112      1      0.6   \n",
       "275   64    1          3     170   227    0        2    155      0      0.6   \n",
       "..   ...  ...        ...     ...   ...  ...      ...    ...    ...      ...   \n",
       "253   51    0          1     120   295    0        2    157      0      0.6   \n",
       "151   42    0          0     102   265    0        2    122      0      0.6   \n",
       "255   42    0          1     120   209    0        0    173      0      0.0   \n",
       "143   64    1          1     125   309    0        0    131      1      1.8   \n",
       "132   29    1          2     130   204    0        2    202      0      0.0   \n",
       "\n",
       "     Slope   Ca  Thal  \n",
       "1        2  3.0     1  \n",
       "142      1  0.0     1  \n",
       "126      3  2.0     2  \n",
       "37       2  1.0     0  \n",
       "275      2  0.0     2  \n",
       "..     ...  ...   ...  \n",
       "253      1  0.0     1  \n",
       "151      2  0.0     1  \n",
       "255      2  0.0     1  \n",
       "143      2  0.0     2  \n",
       "132      1  0.0     1  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "95d300dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "414c1e67",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabhu\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "253463ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c0e01b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f62958c9",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "      <td>58</td>\n",
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       "      <td>318</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "<p>61 rows × 13 columns</p>\n",
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      "text/plain": [
       "     Age  Sex  ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  \\\n",
       "281   47    1          1     130   253    0        0    179      0      0.0   \n",
       "81    53    0          0     130   264    0        2    143      0      0.4   \n",
       "40    65    0          0     150   225    0        2    114      0      1.0   \n",
       "175   57    1          0     152   274    0        0     88      1      1.2   \n",
       "58    54    1          1     125   273    0        2    152      0      0.5   \n",
       "..   ...  ...        ...     ...   ...  ...      ...    ...    ...      ...   \n",
       "285   58    1          0     114   318    0        1    140      0      4.4   \n",
       "294   63    0          0     124   197    0        0    136      1      0.0   \n",
       "133   51    1          0     140   261    0        2    186      1      0.0   \n",
       "196   69    1          3     160   234    1        2    131      0      0.1   \n",
       "75    65    0          1     160   360    0        2    151      0      0.8   \n",
       "\n",
       "     Slope   Ca  Thal  \n",
       "281      1  0.0     1  \n",
       "81       2  0.0     1  \n",
       "40       2  3.0     2  \n",
       "175      2  1.0     2  \n",
       "58       3  1.0     1  \n",
       "..     ...  ...   ...  \n",
       "285      3  3.0     0  \n",
       "294      2  0.0     1  \n",
       "133      1  0.0     1  \n",
       "196      2  1.0     1  \n",
       "75       1  0.0     1  \n",
       "\n",
       "[61 rows x 13 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a2127861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[47,1,1,130,253,0,0,179,0,0.0,1,0.0,1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f3fa89",
   "metadata": {},
   "source": [
    "### KNN Algorithm\n",
    "K- no of nearest neighbors(sqrt(n))\n",
    "1. calculate the euclidean distance\n",
    "2. choose the no of nearest neighbors\n",
    "3. compare with nearest neighbors\n",
    "4. which got the majority vote will be select as final output category\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b05c5bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/Datasets/master/Classification/shirt.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "865706a7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Height    0\n",
       "Weight    0\n",
       "Size      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "17d8b779",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th>6</th>\n",
       "      <td>163</td>\n",
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       "      <td>M</td>\n",
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       "      <td>L</td>\n",
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       "      <td>L</td>\n",
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       "      <td>165</td>\n",
       "      <td>61</td>\n",
       "      <td>L</td>\n",
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       "      <td>L</td>\n",
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       "      <th>12</th>\n",
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       "      <td>62</td>\n",
       "      <td>L</td>\n",
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       "      <th>13</th>\n",
       "      <td>168</td>\n",
       "      <td>63</td>\n",
       "      <td>L</td>\n",
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       "      <th>14</th>\n",
       "      <td>168</td>\n",
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       "      <td>L</td>\n",
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       "      <th>15</th>\n",
       "      <td>170</td>\n",
       "      <td>63</td>\n",
       "      <td>L</td>\n",
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       "      <th>16</th>\n",
       "      <td>170</td>\n",
       "      <td>64</td>\n",
       "      <td>L</td>\n",
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       "      <th>17</th>\n",
       "      <td>170</td>\n",
       "      <td>68</td>\n",
       "      <td>L</td>\n",
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      "text/plain": [
       "    Height  Weight Size\n",
       "0      158      58    M\n",
       "1      158      59    M\n",
       "2      158      63    M\n",
       "3      160      59    M\n",
       "4      160      60    M\n",
       "5      163      60    M\n",
       "6      163      61    M\n",
       "7      160      64    L\n",
       "8      163      64    L\n",
       "9      165      61    L\n",
       "10     165      61    L\n",
       "11     165      62    L\n",
       "12     168      62    L\n",
       "13     168      63    L\n",
       "14     168      66    L\n",
       "15     170      63    L\n",
       "16     170      64    L\n",
       "17     170      68    L"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "403b98e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(\"Size\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "abe7ff33",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df[\"Size\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "15d7cb2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "nb=KNeighborsClassifier(n_neighbors=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "37e13b46",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on KNeighborsClassifier in module sklearn.neighbors._classification object:\n",
      "\n",
      "class KNeighborsClassifier(sklearn.neighbors._base.KNeighborsMixin, sklearn.base.ClassifierMixin, sklearn.neighbors._base.NeighborsBase)\n",
      " |  KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs)\n",
      " |  \n",
      " |  Classifier implementing the k-nearest neighbors vote.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <classification>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  n_neighbors : int, default=5\n",
      " |      Number of neighbors to use by default for :meth:`kneighbors` queries.\n",
      " |  \n",
      " |  weights : {'uniform', 'distance'} or callable, default='uniform'\n",
      " |      weight function used in prediction.  Possible values:\n",
      " |  \n",
      " |      - 'uniform' : uniform weights.  All points in each neighborhood\n",
      " |        are weighted equally.\n",
      " |      - 'distance' : weight points by the inverse of their distance.\n",
      " |        in this case, closer neighbors of a query point will have a\n",
      " |        greater influence than neighbors which are further away.\n",
      " |      - [callable] : a user-defined function which accepts an\n",
      " |        array of distances, and returns an array of the same shape\n",
      " |        containing the weights.\n",
      " |  \n",
      " |  algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'\n",
      " |      Algorithm used to compute the nearest neighbors:\n",
      " |  \n",
      " |      - 'ball_tree' will use :class:`BallTree`\n",
      " |      - 'kd_tree' will use :class:`KDTree`\n",
      " |      - 'brute' will use a brute-force search.\n",
      " |      - 'auto' will attempt to decide the most appropriate algorithm\n",
      " |        based on the values passed to :meth:`fit` method.\n",
      " |  \n",
      " |      Note: fitting on sparse input will override the setting of\n",
      " |      this parameter, using brute force.\n",
      " |  \n",
      " |  leaf_size : int, default=30\n",
      " |      Leaf size passed to BallTree or KDTree.  This can affect the\n",
      " |      speed of the construction and query, as well as the memory\n",
      " |      required to store the tree.  The optimal value depends on the\n",
      " |      nature of the problem.\n",
      " |  \n",
      " |  p : int, default=2\n",
      " |      Power parameter for the Minkowski metric. When p = 1, this is\n",
      " |      equivalent to using manhattan_distance (l1), and euclidean_distance\n",
      " |      (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.\n",
      " |  \n",
      " |  metric : str or callable, default='minkowski'\n",
      " |      the distance metric to use for the tree.  The default metric is\n",
      " |      minkowski, and with p=2 is equivalent to the standard Euclidean\n",
      " |      metric. See the documentation of :class:`DistanceMetric` for a\n",
      " |      list of available metrics.\n",
      " |      If metric is \"precomputed\", X is assumed to be a distance matrix and\n",
      " |      must be square during fit. X may be a :term:`sparse graph`,\n",
      " |      in which case only \"nonzero\" elements may be considered neighbors.\n",
      " |  \n",
      " |  metric_params : dict, default=None\n",
      " |      Additional keyword arguments for the metric function.\n",
      " |  \n",
      " |  n_jobs : int, default=None\n",
      " |      The number of parallel jobs to run for neighbors search.\n",
      " |      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
      " |      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
      " |      for more details.\n",
      " |      Doesn't affect :meth:`fit` method.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  classes_ : array of shape (n_classes,)\n",
      " |      Class labels known to the classifier\n",
      " |  \n",
      " |  effective_metric_ : str or callble\n",
      " |      The distance metric used. It will be same as the `metric` parameter\n",
      " |      or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to\n",
      " |      'minkowski' and `p` parameter set to 2.\n",
      " |  \n",
      " |  effective_metric_params_ : dict\n",
      " |      Additional keyword arguments for the metric function. For most metrics\n",
      " |      will be same with `metric_params` parameter, but may also contain the\n",
      " |      `p` parameter value if the `effective_metric_` attribute is set to\n",
      " |      'minkowski'.\n",
      " |  \n",
      " |  n_samples_fit_ : int\n",
      " |      Number of samples in the fitted data.\n",
      " |  \n",
      " |  outputs_2d_ : bool\n",
      " |      False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit\n",
      " |      otherwise True.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> X = [[0], [1], [2], [3]]\n",
      " |  >>> y = [0, 0, 1, 1]\n",
      " |  >>> from sklearn.neighbors import KNeighborsClassifier\n",
      " |  >>> neigh = KNeighborsClassifier(n_neighbors=3)\n",
      " |  >>> neigh.fit(X, y)\n",
      " |  KNeighborsClassifier(...)\n",
      " |  >>> print(neigh.predict([[1.1]]))\n",
      " |  [0]\n",
      " |  >>> print(neigh.predict_proba([[0.9]]))\n",
      " |  [[0.66666667 0.33333333]]\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  RadiusNeighborsClassifier\n",
      " |  KNeighborsRegressor\n",
      " |  RadiusNeighborsRegressor\n",
      " |  NearestNeighbors\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  See :ref:`Nearest Neighbors <neighbors>` in the online documentation\n",
      " |  for a discussion of the choice of ``algorithm`` and ``leaf_size``.\n",
      " |  \n",
      " |  .. warning::\n",
      " |  \n",
      " |     Regarding the Nearest Neighbors algorithms, if it is found that two\n",
      " |     neighbors, neighbor `k+1` and `k`, have identical distances\n",
      " |     but different labels, the results will depend on the ordering of the\n",
      " |     training data.\n",
      " |  \n",
      " |  https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      KNeighborsClassifier\n",
      " |      sklearn.neighbors._base.KNeighborsMixin\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      sklearn.neighbors._base.NeighborsBase\n",
      " |      sklearn.base.MultiOutputMixin\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y)\n",
      " |      Fit the k-nearest neighbors classifier from the training dataset.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features) or                 (n_samples, n_samples) if metric='precomputed'\n",
      " |          Training data.\n",
      " |      \n",
      " |      y : {array-like, sparse matrix} of shape (n_samples,) or                 (n_samples, n_outputs)\n",
      " |          Target values.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : KNeighborsClassifier\n",
      " |          The fitted k-nearest neighbors classifier.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict the class labels for the provided data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == 'precomputed'\n",
      " |          Test samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y : ndarray of shape (n_queries,) or (n_queries, n_outputs)\n",
      " |          Class labels for each data sample.\n",
      " |  \n",
      " |  predict_proba(self, X)\n",
      " |      Return probability estimates for the test data X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == 'precomputed'\n",
      " |          Test samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      p : ndarray of shape (n_queries, n_classes), or a list of n_outputs\n",
      " |          of such arrays if n_outputs > 1.\n",
      " |          The class probabilities of the input samples. Classes are ordered\n",
      " |          by lexicographic order.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.neighbors._base.KNeighborsMixin:\n",
      " |  \n",
      " |  kneighbors(self, X=None, n_neighbors=None, return_distance=True)\n",
      " |      Finds the K-neighbors of a point.\n",
      " |      \n",
      " |      Returns indices of and distances to the neighbors of each point.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape (n_queries, n_features),             or (n_queries, n_indexed) if metric == 'precomputed',                 default=None\n",
      " |          The query point or points.\n",
      " |          If not provided, neighbors of each indexed point are returned.\n",
      " |          In this case, the query point is not considered its own neighbor.\n",
      " |      \n",
      " |      n_neighbors : int, default=None\n",
      " |          Number of neighbors required for each sample. The default is the\n",
      " |          value passed to the constructor.\n",
      " |      \n",
      " |      return_distance : bool, default=True\n",
      " |          Whether or not to return the distances.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      neigh_dist : ndarray of shape (n_queries, n_neighbors)\n",
      " |          Array representing the lengths to points, only present if\n",
      " |          return_distance=True\n",
      " |      \n",
      " |      neigh_ind : ndarray of shape (n_queries, n_neighbors)\n",
      " |          Indices of the nearest points in the population matrix.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      In the following example, we construct a NearestNeighbors\n",
      " |      class from an array representing our data set and ask who's\n",
      " |      the closest point to [1,1,1]\n",
      " |      \n",
      " |      >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]\n",
      " |      >>> from sklearn.neighbors import NearestNeighbors\n",
      " |      >>> neigh = NearestNeighbors(n_neighbors=1)\n",
      " |      >>> neigh.fit(samples)\n",
      " |      NearestNeighbors(n_neighbors=1)\n",
      " |      >>> print(neigh.kneighbors([[1., 1., 1.]]))\n",
      " |      (array([[0.5]]), array([[2]]))\n",
      " |      \n",
      " |      As you can see, it returns [[0.5]], and [[2]], which means that the\n",
      " |      element is at distance 0.5 and is the third element of samples\n",
      " |      (indexes start at 0). You can also query for multiple points:\n",
      " |      \n",
      " |      >>> X = [[0., 1., 0.], [1., 0., 1.]]\n",
      " |      >>> neigh.kneighbors(X, return_distance=False)\n",
      " |      array([[1],\n",
      " |             [2]]...)\n",
      " |  \n",
      " |  kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity')\n",
      " |      Computes the (weighted) graph of k-Neighbors for points in X\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == 'precomputed',                 default=None\n",
      " |          The query point or points.\n",
      " |          If not provided, neighbors of each indexed point are returned.\n",
      " |          In this case, the query point is not considered its own neighbor.\n",
      " |          For ``metric='precomputed'`` the shape should be\n",
      " |          (n_queries, n_indexed). Otherwise the shape should be\n",
      " |          (n_queries, n_features).\n",
      " |      \n",
      " |      n_neighbors : int, default=None\n",
      " |          Number of neighbors for each sample. The default is the value\n",
      " |          passed to the constructor.\n",
      " |      \n",
      " |      mode : {'connectivity', 'distance'}, default='connectivity'\n",
      " |          Type of returned matrix: 'connectivity' will return the\n",
      " |          connectivity matrix with ones and zeros, in 'distance' the\n",
      " |          edges are Euclidean distance between points.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      A : sparse-matrix of shape (n_queries, n_samples_fit)\n",
      " |          `n_samples_fit` is the number of samples in the fitted data\n",
      " |          `A[i, j]` is assigned the weight of edge that connects `i` to `j`.\n",
      " |          The matrix is of CSR format.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> X = [[0], [3], [1]]\n",
      " |      >>> from sklearn.neighbors import NearestNeighbors\n",
      " |      >>> neigh = NearestNeighbors(n_neighbors=2)\n",
      " |      >>> neigh.fit(X)\n",
      " |      NearestNeighbors(n_neighbors=2)\n",
      " |      >>> A = neigh.kneighbors_graph(X)\n",
      " |      >>> A.toarray()\n",
      " |      array([[1., 0., 1.],\n",
      " |             [0., 1., 1.],\n",
      " |             [1., 0., 1.]])\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      NearestNeighbors.radius_neighbors_graph\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.neighbors._base.KNeighborsMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for `X`.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : dict\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(KNeighborsClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "addd65bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2110f421",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=nb.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "25c51352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5a5cafc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Try to apply heart disease dataset to knn and observe the accuracy"
   ]
  }
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