Knn- abalone.ipynb

{"cells":[{"cell_type":"code","execution_count":8,"id":"91863e24","metadata":{"id":"91863e24","executionInfo":{"status":"ok","timestamp":1654341480657,"user_tz":-330,"elapsed":508,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","pd.pandas.set_option('display.max_columns',None)\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n","from tqdm import tqdm"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LItluHFfbTqu","executionInfo":{"status":"ok","timestamp":1654341485160,"user_tz":-330,"elapsed":3996,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"64f28a7d-e4b9-436c-e216-12b991abddae"},"id":"LItluHFfbTqu","execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","execution_count":10,"id":"2a933ca4","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"2a933ca4","executionInfo":{"status":"ok","timestamp":1654341485161,"user_tz":-330,"elapsed":78,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"6ef63b03-5c30-4808-a421-d566e5f4f684"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n","0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n","1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n","2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n","3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n","4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n","\n","   Shell weight  Rings  \n","0         0.150     15  \n","1         0.070      7  \n","2         0.210      9  \n","3         0.155     10  \n","4         0.055      7  "],"text/html":["\n","  <div id=\"df-3a9686af-c29d-4ad7-bf23-fccda25179a3\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style 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<th>1</th>\n","      <td>M</td>\n","      <td>0.350</td>\n","      <td>0.265</td>\n","      <td>0.090</td>\n","      <td>0.2255</td>\n","      <td>0.0995</td>\n","      <td>0.0485</td>\n","      <td>0.070</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>F</td>\n","      <td>0.530</td>\n","      <td>0.420</td>\n","      <td>0.135</td>\n","      <td>0.6770</td>\n","      <td>0.2565</td>\n","      <td>0.1415</td>\n","      <td>0.210</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>M</td>\n","      <td>0.440</td>\n","      <td>0.365</td>\n","      <td>0.125</td>\n","      <td>0.5160</td>\n","      <td>0.2155</td>\n","      <td>0.1140</td>\n","      <td>0.155</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I</td>\n","      <td>0.330</td>\n","      <td>0.255</td>\n","      <td>0.080</td>\n","      <td>0.2050</td>\n","      <td>0.0895</td>\n","      <td>0.0395</td>\n","      <td>0.055</td>\n","      <td>7</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a9686af-c29d-4ad7-bf23-fccda25179a3')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 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information:"]},{"cell_type":"code","execution_count":11,"id":"6565f226","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6565f226","executionInfo":{"status":"ok","timestamp":1654341485162,"user_tz":-330,"elapsed":75,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"c3293064-471e-417b-a5d3-2b2184523cbd"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 4177 entries, 0 to 4176\n","Data columns (total 9 columns):\n"," #   Column          Non-Null Count  Dtype  \n","---  ------          --------------  -----  \n"," 0   Sex             4177 non-null   object \n"," 1   Length          4177 non-null   float64\n"," 2   Diameter        4177 non-null   float64\n"," 3   Height          4177 non-null   float64\n"," 4   Whole weight    4177 non-null   float64\n"," 5   Shucked weight  4177 non-null   float64\n"," 6   Viscera weight  4177 non-null   float64\n"," 7   Shell weight    4177 non-null   float64\n"," 8   Rings           4177 non-null   int64  \n","dtypes: float64(7), int64(1), object(1)\n","memory usage: 293.8+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":12,"id":"b9c56af5","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"b9c56af5","executionInfo":{"status":"ok","timestamp":1654341485163,"user_tz":-330,"elapsed":70,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"215d3c6b-8d85-400e-fd43-b7726c21b347"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Length     Diameter       Height  Whole weight  Shucked weight  \\\n","count  4177.000000  4177.000000  4177.000000   4177.000000     4177.000000   \n","mean      0.523992     0.407881     0.139516      0.828742        0.359367   \n","std       0.120093     0.099240     0.041827      0.490389        0.221963   \n","min       0.075000     0.055000     0.000000      0.002000        0.001000   \n","25%       0.450000     0.350000     0.115000      0.441500        0.186000   \n","50%       0.545000     0.425000     0.140000      0.799500        0.336000   \n","75%       0.615000     0.480000     0.165000      1.153000        0.502000   \n","max       0.815000     0.650000     1.130000      2.825500        1.488000   \n","\n","       Viscera weight  Shell weight        Rings  \n","count     4177.000000   4177.000000  4177.000000  \n","mean         0.180594      0.238831     9.933684  \n","std          0.109614      0.139203     3.224169  \n","min          0.000500      0.001500     1.000000  \n","25%          0.093500      0.130000     8.000000  \n","50%          0.171000      0.234000     9.000000  \n","75%          0.253000      0.329000    11.000000  \n","max          0.760000      1.005000    29.000000  "],"text/html":["\n","  <div id=\"df-6a33de30-ca5b-4f24-b53f-0aeeb6d2aaf0\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","   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  <td>0.523992</td>\n","      <td>0.407881</td>\n","      <td>0.139516</td>\n","      <td>0.828742</td>\n","      <td>0.359367</td>\n","      <td>0.180594</td>\n","      <td>0.238831</td>\n","      <td>9.933684</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.120093</td>\n","      <td>0.099240</td>\n","      <td>0.041827</td>\n","      <td>0.490389</td>\n","      <td>0.221963</td>\n","      <td>0.109614</td>\n","      <td>0.139203</td>\n","      <td>3.224169</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.075000</td>\n","      <td>0.055000</td>\n","      <td>0.000000</td>\n","      <td>0.002000</td>\n","      <td>0.001000</td>\n","      <td>0.000500</td>\n","      <td>0.001500</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.450000</td>\n","      <td>0.350000</td>\n","      <td>0.115000</td>\n","      <td>0.441500</td>\n","      <td>0.186000</td>\n","      <td>0.093500</td>\n","      <td>0.130000</td>\n","      <td>8.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.545000</td>\n","      <td>0.425000</td>\n","      <td>0.140000</td>\n","      <td>0.799500</td>\n","      <td>0.336000</td>\n","      <td>0.171000</td>\n","      <td>0.234000</td>\n","      <td>9.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.615000</td>\n","      <td>0.480000</td>\n","      <td>0.165000</td>\n","      <td>1.153000</td>\n","      <td>0.502000</td>\n","      <td>0.253000</td>\n","      <td>0.329000</td>\n","      <td>11.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.815000</td>\n","      <td>0.650000</td>\n","      <td>1.130000</td>\n","      <td>2.825500</td>\n","      <td>1.488000</td>\n","      <td>0.760000</td>\n","      <td>1.005000</td>\n","      <td>29.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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  0\n","dtype: int64"]},"metadata":{},"execution_count":14}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":15,"id":"240f1b47","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"240f1b47","executionInfo":{"status":"ok","timestamp":1654341485165,"user_tz":-330,"elapsed":59,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"277907b4-eda1-45b1-a625-2c47af877f7f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n","0    2   0.455     0.365   0.095        0.5140          0.2245   \n","1    2   0.350     0.265   0.090        0.2255          0.0995   \n","2    0   0.530     0.420   0.135        0.6770          0.2565   \n","3    2   0.440     0.365   0.125        0.5160          0.2155   \n","4    1   0.330     0.255   0.080        0.2050          0.0895   \n","\n","   Viscera weight  Shell weight Rings  \n","0          0.1010         0.150     1  \n","1          0.0485         0.070     0  \n","2          0.1415         0.210     0  \n","3          0.1140         0.155     1  \n","4          0.0395         0.055     0  "],"text/html":["\n","  <div id=\"df-0efcd705-2d18-4132-8129-f9833253730a\">\n","    <div class=\"colab-df-container\">\n","      <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>Sex</th>\n","      <th>Length</th>\n","      <th>Diameter</th>\n","      <th>Height</th>\n","      <th>Whole weight</th>\n","      <th>Shucked weight</th>\n","      <th>Viscera weight</th>\n","      <th>Shell weight</th>\n","      <th>Rings</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>0.455</td>\n","      <td>0.365</td>\n","      <td>0.095</td>\n","      <td>0.5140</td>\n","      <td>0.2245</td>\n","      <td>0.1010</td>\n","      <td>0.150</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>0.350</td>\n","      <td>0.265</td>\n","      <td>0.090</td>\n","      <td>0.2255</td>\n","      <td>0.0995</td>\n","      <td>0.0485</td>\n","      <td>0.070</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0.530</td>\n","      <td>0.420</td>\n","      <td>0.135</td>\n","      <td>0.6770</td>\n","      <td>0.2565</td>\n","      <td>0.1415</td>\n","      <td>0.210</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>0.440</td>\n","      <td>0.365</td>\n","      <td>0.125</td>\n","      <td>0.5160</td>\n","      <td>0.2155</td>\n","      <td>0.1140</td>\n","      <td>0.155</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>0.330</td>\n","      <td>0.255</td>\n","      <td>0.080</td>\n","      <td>0.2050</td>\n","      <td>0.0895</td>\n","      <td>0.0395</td>\n","      <td>0.055</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0efcd705-2d18-4132-8129-f9833253730a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":15}],"source":["# df['Sex']----typecasting and label encoding \n","df['Sex']=df['Sex'].astype('category')\n","df['Sex']=df['Sex'].cat.codes\n","# df['Rings']-----typecasting and encoding \n","df['Rings']=np.where(df['Rings'].values >=10,1,0)\n","df['Rings']=df['Rings'].astype('category')\n","df.head()"]},{"cell_type":"code","execution_count":16,"id":"2ac68bd6","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"2ac68bd6","executionInfo":{"status":"ok","timestamp":1654341485166,"user_tz":-330,"elapsed":57,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"af3f328b-90c7-4619-c50e-d12fa725abe7"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 4177 entries, 0 to 4176\n","Data columns (total 9 columns):\n"," #   Column          Non-Null Count  Dtype   \n","---  ------          --------------  -----   \n"," 0   Sex             4177 non-null   int8    \n"," 1   Length          4177 non-null   float64 \n"," 2   Diameter        4177 non-null   float64 \n"," 3   Height          4177 non-null   float64 \n"," 4   Whole weight    4177 non-null   float64 \n"," 5   Shucked weight  4177 non-null   float64 \n"," 6   Viscera weight  4177 non-null   float64 \n"," 7   Shell weight    4177 non-null   float64 \n"," 8   Rings           4177 non-null   category\n","dtypes: category(1), float64(7), int8(1)\n","memory usage: 236.8 KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":17,"id":"b4a28647","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"b4a28647","executionInfo":{"status":"ok","timestamp":1654341485167,"user_tz":-330,"elapsed":53,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"0054cae3-63a5-4781-aeb4-bac6cc0c8e09"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n","0       2   0.455     0.365   0.095        0.5140          0.2245   \n","1       2   0.350     0.265   0.090        0.2255          0.0995   \n","2       0   0.530     0.420   0.135        0.6770          0.2565   \n","3       2   0.440     0.365   0.125        0.5160          0.2155   \n","4       1   0.330     0.255   0.080        0.2050          0.0895   \n","...   ...     ...       ...     ...           ...             ...   \n","4172    0   0.565     0.450   0.165        0.8870          0.3700   \n","4173    2   0.590     0.440   0.135        0.9660          0.4390   \n","4174    2   0.600     0.475   0.205        1.1760          0.5255   \n","4175    0   0.625     0.485   0.150        1.0945          0.5310   \n","4176    2   0.710     0.555   0.195        1.9485          0.9455   \n","\n","      Viscera weight  Shell weight  \n","0             0.1010        0.1500  \n","1             0.0485        0.0700  \n","2             0.1415        0.2100  \n","3             0.1140        0.1550  \n","4             0.0395        0.0550  \n","...              ...           ...  \n","4172          0.2390        0.2490  \n","4173          0.2145        0.2605  \n","4174          0.2875        0.3080  \n","4175          0.2610        0.2960  \n","4176          0.3765        0.4950  \n","\n","[4177 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-1451b7a7-f1c8-413a-ba7e-8e270e68a954\">\n","    <div class=\"colab-df-container\">\n","      <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>Sex</th>\n","      <th>Length</th>\n","      <th>Diameter</th>\n","      <th>Height</th>\n","      <th>Whole weight</th>\n","      <th>Shucked weight</th>\n","      <th>Viscera weight</th>\n","      <th>Shell weight</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>0.455</td>\n","      <td>0.365</td>\n","      <td>0.095</td>\n","      <td>0.5140</td>\n","      <td>0.2245</td>\n","      <td>0.1010</td>\n","      <td>0.1500</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>0.350</td>\n","      <td>0.265</td>\n","      <td>0.090</td>\n","      <td>0.2255</td>\n","      <td>0.0995</td>\n","      <td>0.0485</td>\n","      <td>0.0700</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0.530</td>\n","      <td>0.420</td>\n","      <td>0.135</td>\n","      <td>0.6770</td>\n","      <td>0.2565</td>\n","      <td>0.1415</td>\n","      <td>0.2100</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>0.440</td>\n","      <td>0.365</td>\n","      <td>0.125</td>\n","      <td>0.5160</td>\n","      <td>0.2155</td>\n","      <td>0.1140</td>\n","      <td>0.1550</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>0.330</td>\n","      <td>0.255</td>\n","      <td>0.080</td>\n","      <td>0.2050</td>\n","      <td>0.0895</td>\n","      <td>0.0395</td>\n","      <td>0.0550</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","    </tr>\n","    <tr>\n","      <th>4172</th>\n","      <td>0</td>\n","      <td>0.565</td>\n","      <td>0.450</td>\n","      <td>0.165</td>\n","      <td>0.8870</td>\n","      <td>0.3700</td>\n","      <td>0.2390</td>\n","      <td>0.2490</td>\n","    </tr>\n","    <tr>\n","      <th>4173</th>\n","      <td>2</td>\n","      <td>0.590</td>\n","      <td>0.440</td>\n","      <td>0.135</td>\n","      <td>0.9660</td>\n","      <td>0.4390</td>\n","      <td>0.2145</td>\n","      <td>0.2605</td>\n","    </tr>\n","    <tr>\n","      <th>4174</th>\n","      <td>2</td>\n","      <td>0.600</td>\n","      <td>0.475</td>\n","      <td>0.205</td>\n","      <td>1.1760</td>\n","      <td>0.5255</td>\n","      <td>0.2875</td>\n","      <td>0.3080</td>\n","    </tr>\n","    <tr>\n","      <th>4175</th>\n","      <td>0</td>\n","      <td>0.625</td>\n","      <td>0.485</td>\n","      <td>0.150</td>\n","      <td>1.0945</td>\n","      <td>0.5310</td>\n","      <td>0.2610</td>\n","      <td>0.2960</td>\n","    </tr>\n","    <tr>\n","      <th>4176</th>\n","      <td>2</td>\n","      <td>0.710</td>\n","      <td>0.555</td>\n","      <td>0.195</td>\n","      <td>1.9485</td>\n","      <td>0.9455</td>\n","      <td>0.3765</td>\n","      <td>0.4950</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4177 rows × 8 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1451b7a7-f1c8-413a-ba7e-8e270e68a954')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","    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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":17}],"source":["x=df.iloc[:,:-1]\n","x"]},{"cell_type":"code","execution_count":18,"id":"ef42f4c7","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ef42f4c7","executionInfo":{"status":"ok","timestamp":1654341485167,"user_tz":-330,"elapsed":51,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"7d308ae4-587a-441e-aec7-da5add451562"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       1\n","1       0\n","2       0\n","3       1\n","4       0\n","       ..\n","4172    1\n","4173    1\n","4174    0\n","4175    1\n","4176    1\n","Name: Rings, Length: 4177, dtype: category\n","Categories (2, int64): [0, 1]"]},"metadata":{},"execution_count":18}],"source":["y=df.iloc[:,-1]\n","y"]},{"cell_type":"code","execution_count":18,"id":"cc89ae7a","metadata":{"id":"cc89ae7a","executionInfo":{"status":"ok","timestamp":1654341485168,"user_tz":-330,"elapsed":47,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":[""]},{"cell_type":"markdown","id":"228c8da9","metadata":{"id":"228c8da9"},"source":["### train test data spliting:"]},{"cell_type":"code","execution_count":19,"id":"e219d6a8","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"e219d6a8","executionInfo":{"status":"ok","timestamp":1654341485168,"user_tz":-330,"elapsed":46,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"3f8bfc50-b290-457e-9022-d72d6127d255"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["((2923, 8), (1254, 8), (2923,), (1254,))"]},"metadata":{},"execution_count":19}],"source":["xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,stratify=y)\n","xtrain.shape,xtest.shape,ytrain.shape,ytest.shape"]},{"cell_type":"markdown","id":"29909bee","metadata":{"id":"29909bee"},"source":["### Feature Scaling ----> MinMax Scaler"]},{"cell_type":"code","execution_count":20,"id":"68dd4106","metadata":{"id":"68dd4106","executionInfo":{"status":"ok","timestamp":1654341485169,"user_tz":-330,"elapsed":42,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":["minmax=MinMaxScaler()\n","xtrain=minmax.fit_transform(xtrain)\n","xtest=minmax.fit_transform(xtest)"]},{"cell_type":"code","execution_count":21,"id":"858510b7","metadata":{"id":"858510b7","executionInfo":{"status":"ok","timestamp":1654341485170,"user_tz":-330,"elapsed":42,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":["knn=KNeighborsClassifier()\n","model=knn.fit(xtrain,ytrain)"]},{"cell_type":"code","execution_count":22,"id":"9ff695ce","metadata":{"id":"9ff695ce","executionInfo":{"status":"ok","timestamp":1654341485171,"user_tz":-330,"elapsed":43,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":["pred_train=knn.predict(xtrain)\n","pred_test=knn.predict(xtest)"]},{"cell_type":"code","execution_count":23,"id":"17f67ccc","metadata":{"id":"17f67ccc","executionInfo":{"status":"ok","timestamp":1654341485171,"user_tz":-330,"elapsed":41,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":["def metrics(a,b):\n","    cm=confusion_matrix(a,b)\n","    print('Confusion Matrix:\\n',cm)\n","    print('-'*100)\n","    cr=classification_report(a,b)\n","    print('Class Report:\\n',cr)\n","    print('-'*100)\n","    acc=accuracy_score(a,b)\n","    print('Accuracy Score:\\n',acc)\n","    print('-'*100)\n","    "]},{"cell_type":"markdown","id":"a1c9497c","metadata":{"id":"a1c9497c"},"source":["###  KNN Report:"]},{"cell_type":"code","execution_count":24,"id":"35a602cb","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"35a602cb","executionInfo":{"status":"ok","timestamp":1654341485173,"user_tz":-330,"elapsed":43,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"35abd03d-189e-4de7-af34-84c686d08e1f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"," [[1234  233]\n"," [ 208 1248]]\n","----------------------------------------------------------------------------------------------------\n","Class Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.86      0.84      0.85      1467\n","           1       0.84      0.86      0.85      1456\n","\n","    accuracy                           0.85      2923\n","   macro avg       0.85      0.85      0.85      2923\n","weighted avg       0.85      0.85      0.85      2923\n","\n","----------------------------------------------------------------------------------------------------\n","Accuracy Score:\n"," 0.8491276086212796\n","----------------------------------------------------------------------------------------------------\n"]}],"source":["metrics(ytrain,pred_train)"]},{"cell_type":"code","execution_count":25,"id":"e85ad827","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e85ad827","executionInfo":{"status":"ok","timestamp":1654341486113,"user_tz":-330,"elapsed":978,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"398ca179-780a-471a-a7f2-061f59cf9870"},"outputs":[{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"," [[268 361]\n"," [ 39 586]]\n","----------------------------------------------------------------------------------------------------\n","Class Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.87      0.43      0.57       629\n","           1       0.62      0.94      0.75       625\n","\n","    accuracy                           0.68      1254\n","   macro avg       0.75      0.68      0.66      1254\n","weighted avg       0.75      0.68      0.66      1254\n","\n","----------------------------------------------------------------------------------------------------\n","Accuracy Score:\n"," 0.6810207336523126\n","----------------------------------------------------------------------------------------------------\n"]}],"source":["metrics(ytest,pred_test)"]},{"cell_type":"markdown","id":"8ecdeafd","metadata":{"id":"8ecdeafd"},"source":["Doing Hyperparameter tuning"]},{"cell_type":"code","execution_count":26,"id":"ecb41912","metadata":{"id":"ecb41912","executionInfo":{"status":"ok","timestamp":1654341486114,"user_tz":-330,"elapsed":32,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":["knn=KNeighborsClassifier(n_neighbors=3)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train1=knn.predict(xtrain)\n","pred_test1=knn.predict(xtest)"]},{"cell_type":"code","execution_count":27,"id":"fa88efe5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fa88efe5","executionInfo":{"status":"ok","timestamp":1654341486114,"user_tz":-330,"elapsed":32,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"b54b509b-6575-486e-a563-3813c7919922"},"outputs":[{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"," [[1265  202]\n"," [ 180 1276]]\n","----------------------------------------------------------------------------------------------------\n","Class Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.88      0.86      0.87      1467\n","           1       0.86      0.88      0.87      1456\n","\n","    accuracy                           0.87      2923\n","   macro avg       0.87      0.87      0.87      2923\n","weighted avg       0.87      0.87      0.87      2923\n","\n","----------------------------------------------------------------------------------------------------\n","Accuracy Score:\n"," 0.8693123503250085\n","----------------------------------------------------------------------------------------------------\n"]}],"source":["metrics(ytrain,pred_train1)"]},{"cell_type":"code","execution_count":28,"id":"247917c3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"247917c3","executionInfo":{"status":"ok","timestamp":1654341486115,"user_tz":-330,"elapsed":26,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"11b76122-b7de-4e9b-8ac9-74682f57f4d4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"," [[295 334]\n"," [ 50 575]]\n","----------------------------------------------------------------------------------------------------\n","Class Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.86      0.47      0.61       629\n","           1       0.63      0.92      0.75       625\n","\n","    accuracy                           0.69      1254\n","   macro avg       0.74      0.69      0.68      1254\n","weighted avg       0.74      0.69      0.68      1254\n","\n","----------------------------------------------------------------------------------------------------\n","Accuracy Score:\n"," 0.69377990430622\n","----------------------------------------------------------------------------------------------------\n"]}],"source":["metrics(ytest,pred_test1)"]},{"cell_type":"code","execution_count":29,"id":"436398e2","metadata":{"id":"436398e2","executionInfo":{"status":"ok","timestamp":1654341486115,"user_tz":-330,"elapsed":20,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"outputs":[],"source":["knn=KNeighborsClassifier(n_neighbors=5)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train2=knn.predict(xtrain)\n","pred_test2=knn.predict(xtest)"]},{"cell_type":"code","execution_count":30,"id":"71f7bd66","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"71f7bd66","executionInfo":{"status":"ok","timestamp":1654341486116,"user_tz":-330,"elapsed":20,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"eec43bed-f927-4db4-aa14-2e144a964969"},"outputs":[{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"," [[1234  233]\n"," [ 208 1248]]\n","----------------------------------------------------------------------------------------------------\n","Class Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.86      0.84      0.85      1467\n","           1       0.84      0.86      0.85      1456\n","\n","    accuracy                           0.85      2923\n","   macro avg       0.85      0.85      0.85      2923\n","weighted avg       0.85      0.85      0.85      2923\n","\n","----------------------------------------------------------------------------------------------------\n","Accuracy Score:\n"," 0.8491276086212796\n","----------------------------------------------------------------------------------------------------\n"]}],"source":["metrics(ytrain,pred_train2)"]},{"cell_type":"code","execution_count":31,"id":"781f15c1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"781f15c1","executionInfo":{"status":"ok","timestamp":1654341486116,"user_tz":-330,"elapsed":15,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"8fba4c2e-1240-4c1a-a443-ec6b3dc83a0e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"," [[268 361]\n"," [ 39 586]]\n","----------------------------------------------------------------------------------------------------\n","Class Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.87      0.43      0.57       629\n","           1       0.62      0.94      0.75       625\n","\n","    accuracy                           0.68      1254\n","   macro avg       0.75      0.68      0.66      1254\n","weighted avg       0.75      0.68      0.66      1254\n","\n","----------------------------------------------------------------------------------------------------\n","Accuracy Score:\n"," 0.6810207336523126\n","----------------------------------------------------------------------------------------------------\n"]}],"source":["metrics(ytest,pred_test2)"]},{"cell_type":"code","execution_count":32,"id":"6dae6f31","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":645},"id":"6dae6f31","executionInfo":{"status":"ok","timestamp":1654341488736,"user_tz":-330,"elapsed":2631,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"8880ef35-5022-4754-cbf9-88863dd3114f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Train_accuracy  Test_accuracy\n","0         1.000000       0.687400\n","1         0.865891       0.706539\n","2         0.869312       0.693780\n","3         0.843996       0.700159\n","4         0.849128       0.681021\n","5         0.838522       0.693780\n","6         0.841601       0.675439\n","7         0.827232       0.681818\n","8         0.828259       0.679426\n","9         0.824153       0.696172\n","10        0.824495       0.684211\n","11        0.825864       0.692982\n","12        0.823811       0.687400\n","13        0.819706       0.692982\n","14        0.821074       0.688198\n","15        0.815943       0.692185\n","16        0.819706       0.685805\n","17        0.815258       0.691388\n","18        0.823811       0.688198"],"text/html":["\n","  <div id=\"df-0ad37f5a-f96d-4d14-bbd0-eacc5113b299\">\n","    <div class=\"colab-df-container\">\n","      <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>Train_accuracy</th>\n","      <th>Test_accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.000000</td>\n","      <td>0.687400</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.865891</td>\n","      <td>0.706539</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.869312</td>\n","      <td>0.693780</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.843996</td>\n","      <td>0.700159</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.849128</td>\n","      <td>0.681021</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.838522</td>\n","      <td>0.693780</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.841601</td>\n","      <td>0.675439</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.827232</td>\n","      <td>0.681818</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.828259</td>\n","      <td>0.679426</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.824153</td>\n","      <td>0.696172</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.824495</td>\n","      <td>0.684211</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.825864</td>\n","      <td>0.692982</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.823811</td>\n","      <td>0.687400</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.819706</td>\n","      <td>0.692982</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0.821074</td>\n","      <td>0.688198</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>0.815943</td>\n","      <td>0.692185</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0.819706</td>\n","      <td>0.685805</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>0.815258</td>\n","      <td>0.691388</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>0.823811</td>\n","      <td>0.688198</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ad37f5a-f96d-4d14-bbd0-eacc5113b299')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      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</style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0ad37f5a-f96d-4d14-bbd0-eacc5113b299 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-0ad37f5a-f96d-4d14-bbd0-eacc5113b299');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":32}],"source":["#iteratively doing for k(neighbours)\n","accuracy=pd.DataFrame(columns=['Train_accuracy','Test_accuracy' ])\n","\n","for n in range(1,20):\n","    knn=KNeighborsClassifier(n_neighbors=n)\n","    model=knn.fit(xtrain,ytrain)\n","    \n","    pred_train= model.predict(xtrain)\n","    pred_test = model.predict(xtest)\n","    \n","    train_acc=accuracy_score(ytrain,pred_train)\n","    test_acc=accuracy_score(ytest,pred_test)\n","    \n","    accuracy=accuracy.append({'Train_accuracy':train_acc,'Test_accuracy': test_acc},ignore_index=True)\n","    \n","accuracy"]},{"cell_type":"code","execution_count":33,"id":"ca5d8b3c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ca5d8b3c","executionInfo":{"status":"ok","timestamp":1654341534422,"user_tz":-330,"elapsed":45717,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"9f1994a4-b1b5-427b-c1b7-f392c467c373"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 200/200 [00:46<00:00,  4.34it/s]\n"]}],"source":["Result=pd.DataFrame(columns=['K','Training','Testing'])\n","\n","for k in tqdm(range(1,201)):\n","              knn=KNeighborsClassifier(n_neighbors=k)\n","              model=knn.fit(xtrain,ytrain)\n","    \n","              pred_train = model.predict(xtrain)\n","              pred_test = model.predict(xtest)\n","              \n","              Result=Result.append({'K':k,'Training':accuracy_score(ytrain,pred_train),'Testing':accuracy_score(ytest,pred_test)},ignore_index=True)\n","              "]},{"cell_type":"code","execution_count":34,"id":"2d4dd2dc","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":623},"id":"2d4dd2dc","executionInfo":{"status":"ok","timestamp":1654341535424,"user_tz":-330,"elapsed":1033,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"8638e1ec-6a1e-4255-eb6d-ffcc8940eeff"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fbb1943b590>"]},"metadata":{},"execution_count":34},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize=(10,10))\n","sns.lineplot(data= Result, x= 'K', y= 'Training')\n","sns.lineplot(data= Result, x= 'K', y= 'Testing')"]},{"cell_type":"code","execution_count":35,"id":"a410bef4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a410bef4","executionInfo":{"status":"ok","timestamp":1654341538471,"user_tz":-330,"elapsed":3101,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"5c9566cf-6ce0-4b49-b2a0-b0882191b88c"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 25/25 [00:03<00:00,  6.63it/s]\n"]}],"source":["Result=pd.DataFrame(columns=['K','Training','Testing'])\n","\n","for k in tqdm(range(1,26)):\n","              knn=KNeighborsClassifier(n_neighbors=k)\n","              model=knn.fit(xtrain,ytrain)\n","    \n","              pred_train = model.predict(xtrain)\n","              pred_test = model.predict(xtest)\n","              \n","              Result=Result.append({'K':k,'Training':accuracy_score(ytrain,pred_train),'Testing':accuracy_score(ytest,pred_test)},ignore_index=True)\n","              "]},{"cell_type":"code","execution_count":36,"id":"70a2f017","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":623},"id":"70a2f017","executionInfo":{"status":"ok","timestamp":1654341539119,"user_tz":-330,"elapsed":84,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"3d014521-8f15-4a22-b0d2-6448d72ce0bf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fbb183cb790>"]},"metadata":{},"execution_count":36},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize=(10,10))\n","sns.lineplot(data= Result, x= 'K', y= 'Training')\n","sns.lineplot(data= Result, x= 'K', y= 'Testing')"]},{"cell_type":"code","execution_count":37,"id":"1d24edd4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1d24edd4","executionInfo":{"status":"ok","timestamp":1654341539122,"user_tz":-330,"elapsed":78,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"2cbfd1a0-f516-4a20-d4bc-2fb0cc9fa943"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for Train:  0.8491276086212796\n","Accuracy for Test:  0.6810207336523126\n"]}],"source":["knn=KNeighborsClassifier(n_neighbors=5)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train=knn.predict(xtrain)\n","pred_test=knn.predict(xtest)\n","\n","print('Accuracy for Train: ',accuracy_score(ytrain, pred_train))\n","print('Accuracy for Test: ',accuracy_score(ytest, pred_test))"]},{"cell_type":"code","execution_count":38,"id":"f07ec299","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f07ec299","executionInfo":{"status":"ok","timestamp":1654341539124,"user_tz":-330,"elapsed":73,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"1665b296-5166-4fb1-f92c-1466214face2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for Train:  0.843995894628806\n","Accuracy for Test:  0.7001594896331739\n"]}],"source":["knn=KNeighborsClassifier(n_neighbors=4)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train=knn.predict(xtrain)\n","pred_test=knn.predict(xtest)\n","\n","print('Accuracy for Train: ',accuracy_score(ytrain, pred_train))\n","print('Accuracy for Test: ',accuracy_score(ytest, pred_test))"]},{"cell_type":"code","execution_count":39,"id":"7d3d35b5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7d3d35b5","executionInfo":{"status":"ok","timestamp":1654341539639,"user_tz":-330,"elapsed":582,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"c65ff68f-c6b9-4d67-e684-f641eb20c83a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for Train:  0.8658912076633596\n","Accuracy for Test:  0.7065390749601276\n"]}],"source":["knn=KNeighborsClassifier(n_neighbors=2)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train=knn.predict(xtrain)\n","pred_test=knn.predict(xtest)\n","\n","print('Accuracy for Train: ',accuracy_score(ytrain, pred_train))\n","print('Accuracy for Test: ',accuracy_score(ytest, pred_test))"]},{"cell_type":"code","execution_count":40,"id":"f63494d1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f63494d1","executionInfo":{"status":"ok","timestamp":1654341539640,"user_tz":-330,"elapsed":15,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"9ef386dc-6db3-41a8-d6cc-ffa7c6525ce8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for Train:  0.8693123503250085\n","Accuracy for Test:  0.69377990430622\n"]}],"source":["knn=KNeighborsClassifier(n_neighbors=3)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train=knn.predict(xtrain)\n","pred_test=knn.predict(xtest)\n","\n","print('Accuracy for Train: ',accuracy_score(ytrain, pred_train))\n","print('Accuracy for Test: ',accuracy_score(ytest, pred_test))"]},{"cell_type":"code","execution_count":41,"id":"e67ad7f4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e67ad7f4","executionInfo":{"status":"ok","timestamp":1654341539641,"user_tz":-330,"elapsed":12,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"4327b7ac-fee6-41ea-fb15-6b80dbc4f0e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for Train:  0.8159425248032843\n","Accuracy for Test:  0.6921850079744817\n"]}],"source":["knn=KNeighborsClassifier(n_neighbors=16)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train=knn.predict(xtrain)\n","pred_test=knn.predict(xtest)\n","\n","print('Accuracy for Train: ',accuracy_score(ytrain, pred_train))\n","print('Accuracy for Test: ',accuracy_score(ytest, pred_test))"]},{"cell_type":"code","execution_count":42,"id":"50e1948a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"50e1948a","executionInfo":{"status":"ok","timestamp":1654341540219,"user_tz":-330,"elapsed":586,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"08ae4f40-eaf6-42b5-c7de-5240565c33f7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy for Train:  0.8197057817310982\n","Accuracy for Test:  0.6858054226475279\n"]}],"source":["knn=KNeighborsClassifier(n_neighbors=17)\n","model=knn.fit(xtrain,ytrain)\n","\n","pred_train=knn.predict(xtrain)\n","pred_test=knn.predict(xtest)\n","\n","print('Accuracy for Train: ',accuracy_score(ytrain, pred_train))\n","print('Accuracy for Test: ',accuracy_score(ytest, pred_test))"]},{"cell_type":"code","execution_count":43,"id":"608d345c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"608d345c","executionInfo":{"status":"ok","timestamp":1654341541956,"user_tz":-330,"elapsed":1754,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"ecf6feb3-43e5-4e67-d7cd-c1297089fcd7"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 14/14 [00:02<00:00,  6.16it/s]\n"]}],"source":["Result=pd.DataFrame(columns=['K','Training','Testing'])\n","\n","for k in tqdm(range(1,15)):\n","              knn=KNeighborsClassifier(n_neighbors=k)\n","              model=knn.fit(xtrain,ytrain)\n","    \n","              pred_train = model.predict(xtrain)\n","              pred_test = model.predict(xtest)\n","              \n","              Result=Result.append({'K':k,'Training':accuracy_score(ytrain,pred_train),'Testing':accuracy_score(ytest,pred_test)},ignore_index=True)"]},{"cell_type":"code","execution_count":44,"id":"047e8b34","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":623},"id":"047e8b34","executionInfo":{"status":"ok","timestamp":1654341542623,"user_tz":-330,"elapsed":683,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}},"outputId":"3683629a-3b25-4067-b3b7-9a0a7dd663d2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fbb17f07650>"]},"metadata":{},"execution_count":44},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize=(10,10))\n","sns.lineplot(data= Result, x= 'K', y= 'Training')\n","sns.lineplot(data= Result, x= 'K', y= 'Testing')"]},{"cell_type":"code","execution_count":44,"id":"7081fbe0","metadata":{"id":"7081fbe0","executionInfo":{"status":"ok","timestamp":1654341542630,"user_tz":-330,"elapsed":41,"user":{"displayName":"Srilekya G S Leki","userId":"09512986609150534149"}}},"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":"3.8.8"},"colab":{"name":"Knn- abalone.ipynb","provenance":[],"collapsed_sections":[]}},"nbformat":4,"nbformat_minor":5}