class6.1 (2).ipynb

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  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF=pd.read_csv(r'E:\\Things I Did\\pyclasses\\bodyfat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Bodyfat  Age  Weight  Height  Neck  Chest  Abdomen  Ankle  Biceps  Wrist\n",
       "0      32.3   41  247.25   73.50  42.1  117.0    115.6   26.3    37.3   19.7\n",
       "1      22.5   31  177.25   71.50  36.2  101.1     92.4   24.6    30.1   18.2\n",
       "2      22.0   42  156.25   69.00  35.5   97.8     86.0   24.0    31.2   17.4\n",
       "3      12.3   23  154.25   67.75  36.2   93.1     85.2   21.9    32.0   17.1\n",
       "4      20.5   46  177.00   70.00  37.2   99.7     95.6   22.5    29.1   17.7\n",
       "..      ...  ...     ...     ...   ...    ...      ...    ...     ...    ...\n",
       "95     20.1   48  177.25   72.75  36.8   96.0     90.0   22.8    29.9   18.1\n",
       "96     29.4   43  187.75   74.00  37.7   97.8     98.6   23.8    34.3   17.7\n",
       "97     13.0   33  184.25   68.75  40.7   98.9     92.1   23.5    33.5   19.7\n",
       "98     25.2   26  223.00   70.25  40.6  114.1    106.8   24.7    36.0   18.4\n",
       "99      4.0   47  127.50   66.75  34.0   83.4     70.4   21.9    26.8   16.8\n",
       "\n",
       "[100 rows x 10 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Bodyfat</th>\n      <th>Age</th>\n      <th>Weight</th>\n      <th>Height</th>\n      <th>Neck</th>\n      <th>Chest</th>\n      <th>Abdomen</th>\n      <th>Ankle</th>\n      <th>Biceps</th>\n      <th>Wrist</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>32.3</td>\n      <td>41</td>\n      <td>247.25</td>\n      <td>73.50</td>\n      <td>42.1</td>\n      <td>117.0</td>\n      <td>115.6</td>\n      <td>26.3</td>\n      <td>37.3</td>\n      <td>19.7</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>22.5</td>\n      <td>31</td>\n      <td>177.25</td>\n      <td>71.50</td>\n      <td>36.2</td>\n      <td>101.1</td>\n      <td>92.4</td>\n      <td>24.6</td>\n      <td>30.1</td>\n      <td>18.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>22.0</td>\n      <td>42</td>\n      <td>156.25</td>\n      <td>69.00</td>\n      <td>35.5</td>\n      <td>97.8</td>\n      <td>86.0</td>\n      <td>24.0</td>\n      <td>31.2</td>\n      <td>17.4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>12.3</td>\n      <td>23</td>\n      <td>154.25</td>\n      <td>67.75</td>\n      <td>36.2</td>\n      <td>93.1</td>\n      <td>85.2</td>\n      <td>21.9</td>\n      <td>32.0</td>\n      <td>17.1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20.5</td>\n      <td>46</td>\n      <td>177.00</td>\n      <td>70.00</td>\n      <td>37.2</td>\n      <td>99.7</td>\n      <td>95.6</td>\n      <td>22.5</td>\n      <td>29.1</td>\n      <td>17.7</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>20.1</td>\n      <td>48</td>\n      <td>177.25</td>\n      <td>72.75</td>\n      <td>36.8</td>\n      <td>96.0</td>\n      <td>90.0</td>\n      <td>22.8</td>\n      <td>29.9</td>\n      <td>18.1</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>29.4</td>\n      <td>43</td>\n      <td>187.75</td>\n      <td>74.00</td>\n      <td>37.7</td>\n      <td>97.8</td>\n      <td>98.6</td>\n      <td>23.8</td>\n      <td>34.3</td>\n      <td>17.7</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>13.0</td>\n      <td>33</td>\n      <td>184.25</td>\n      <td>68.75</td>\n      <td>40.7</td>\n      <td>98.9</td>\n      <td>92.1</td>\n      <td>23.5</td>\n      <td>33.5</td>\n      <td>19.7</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>25.2</td>\n      <td>26</td>\n      <td>223.00</td>\n      <td>70.25</td>\n      <td>40.6</td>\n      <td>114.1</td>\n      <td>106.8</td>\n      <td>24.7</td>\n      <td>36.0</td>\n      <td>18.4</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>4.0</td>\n      <td>47</td>\n      <td>127.50</td>\n      <td>66.75</td>\n      <td>34.0</td>\n      <td>83.4</td>\n      <td>70.4</td>\n      <td>21.9</td>\n      <td>26.8</td>\n      <td>16.8</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 10 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Age  Weight  Height  Neck  Chest  Abdomen  Ankle  Biceps  Wrist  IsFat\n",
       "0    41  247.25   73.50  42.1  117.0    115.6   26.3    37.3   19.7      1\n",
       "1    31  177.25   71.50  36.2  101.1     92.4   24.6    30.1   18.2      1\n",
       "2    42  156.25   69.00  35.5   97.8     86.0   24.0    31.2   17.4      1\n",
       "3    23  154.25   67.75  36.2   93.1     85.2   21.9    32.0   17.1      0\n",
       "4    46  177.00   70.00  37.2   99.7     95.6   22.5    29.1   17.7      1\n",
       "..  ...     ...     ...   ...    ...      ...    ...     ...    ...    ...\n",
       "95   48  177.25   72.75  36.8   96.0     90.0   22.8    29.9   18.1      1\n",
       "96   43  187.75   74.00  37.7   97.8     98.6   23.8    34.3   17.7      1\n",
       "97   33  184.25   68.75  40.7   98.9     92.1   23.5    33.5   19.7      0\n",
       "98   26  223.00   70.25  40.6  114.1    106.8   24.7    36.0   18.4      1\n",
       "99   47  127.50   66.75  34.0   83.4     70.4   21.9    26.8   16.8      0\n",
       "\n",
       "[100 rows x 10 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Weight</th>\n      <th>Height</th>\n      <th>Neck</th>\n      <th>Chest</th>\n      <th>Abdomen</th>\n      <th>Ankle</th>\n      <th>Biceps</th>\n      <th>Wrist</th>\n      <th>IsFat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>41</td>\n      <td>247.25</td>\n      <td>73.50</td>\n      <td>42.1</td>\n      <td>117.0</td>\n      <td>115.6</td>\n      <td>26.3</td>\n      <td>37.3</td>\n      <td>19.7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>31</td>\n      <td>177.25</td>\n      <td>71.50</td>\n      <td>36.2</td>\n      <td>101.1</td>\n      <td>92.4</td>\n      <td>24.6</td>\n      <td>30.1</td>\n      <td>18.2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>42</td>\n      <td>156.25</td>\n      <td>69.00</td>\n      <td>35.5</td>\n      <td>97.8</td>\n      <td>86.0</td>\n      <td>24.0</td>\n      <td>31.2</td>\n      <td>17.4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23</td>\n      <td>154.25</td>\n      <td>67.75</td>\n      <td>36.2</td>\n      <td>93.1</td>\n      <td>85.2</td>\n      <td>21.9</td>\n      <td>32.0</td>\n      <td>17.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>46</td>\n      <td>177.00</td>\n      <td>70.00</td>\n      <td>37.2</td>\n      <td>99.7</td>\n      <td>95.6</td>\n      <td>22.5</td>\n      <td>29.1</td>\n      <td>17.7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>48</td>\n      <td>177.25</td>\n      <td>72.75</td>\n      <td>36.8</td>\n      <td>96.0</td>\n      <td>90.0</td>\n      <td>22.8</td>\n      <td>29.9</td>\n      <td>18.1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>43</td>\n      <td>187.75</td>\n      <td>74.00</td>\n      <td>37.7</td>\n      <td>97.8</td>\n      <td>98.6</td>\n      <td>23.8</td>\n      <td>34.3</td>\n      <td>17.7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>33</td>\n      <td>184.25</td>\n      <td>68.75</td>\n      <td>40.7</td>\n      <td>98.9</td>\n      <td>92.1</td>\n      <td>23.5</td>\n      <td>33.5</td>\n      <td>19.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>26</td>\n      <td>223.00</td>\n      <td>70.25</td>\n      <td>40.6</td>\n      <td>114.1</td>\n      <td>106.8</td>\n      <td>24.7</td>\n      <td>36.0</td>\n      <td>18.4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>47</td>\n      <td>127.50</td>\n      <td>66.75</td>\n      <td>34.0</td>\n      <td>83.4</td>\n      <td>70.4</td>\n      <td>21.9</td>\n      <td>26.8</td>\n      <td>16.8</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 10 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "# IsFat 1 if bodyfat >= 20 else 0\n",
    "DF['IsFat'] = [1 if i>=20 else 0 for i in DF['Bodyfat']]\n",
    "DF.drop(columns = ['Bodyfat'],inplace = True)\n",
    "#DF.drop(columns = ['Age'],inplace = True)\n",
    "DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0     1\n",
       "1     1\n",
       "2     1\n",
       "3     0\n",
       "4     1\n",
       "     ..\n",
       "95    1\n",
       "96    1\n",
       "97    0\n",
       "98    1\n",
       "99    0\n",
       "Name: IsFat, Length: 100, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "source": [
    "X=DF.iloc[:,0:-1]\n",
    "Y=DF.iloc[:,-1]\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, Ytrain, Ytest=train_test_split(X,Y,test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=2, weights='distance')"
      ]
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "model=KNeighborsClassifier(n_neighbors=2,weights='distance')\n",
    "model.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1],\n",
       "      dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "source": [
    "Ypred=model.predict(Xtest)\n",
    "Ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[2 9]\n [0 9]]\n"
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "disp=plot_confusion_matrix(model,Xtest,Ytest)\n",
    "print(disp.confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}