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colab_files_to_train_models/Multiple disease prediction system - Parkinsons.ipynb

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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Multiple disease prediction system - Parkinsons.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPgH5xu9ZLOpcMFNkcpInRX"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"9B5Zl1UOBMAJ"},"source":["Importing the Dependencies"]},{"cell_type":"code","metadata":{"id":"YOCpZ1Vm6cfW","executionInfo":{"status":"ok","timestamp":1653200307851,"user_tz":-330,"elapsed":2162,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn import svm\n","from sklearn.metrics import accuracy_score"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PZm-USrtB_q4"},"source":["Data Collection & Analysis"]},{"cell_type":"code","metadata":{"id":"5YC2lGuVBiZA","executionInfo":{"status":"ok","timestamp":1653200307854,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the data from csv file to a Pandas DataFrame\n","parkinsons_data = pd.read_csv('/content/parkinsons.csv')"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":299},"id":"Iw8z6w60Djd2","executionInfo":{"status":"ok","timestamp":1653200307855,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"ca177b83-79f9-46c5-89c0-42985b1923ba"},"source":["# printing the first 5 rows of the dataframe\n","parkinsons_data.head()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n","0  phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n","1  phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n","2  phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n","3  phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n","4  phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n","\n","   MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n","0           0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n","1           0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n","2           0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n","3           0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n","4           0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n","\n","   Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n","0      0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n","1      0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n","2      0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n","3      0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n","4      0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n","\n","    spread2        D2       PPE  \n","0  0.266482  2.301442  0.284654  \n","1  0.335590  2.486855  0.368674  \n","2  0.311173  2.342259  0.332634  \n","3  0.334147  2.405554  0.368975  \n","4  0.234513  2.332180  0.410335  \n","\n","[5 rows x 24 columns]"],"text/html":["\n","  <div id=\"df-09ab8692-d189-4aac-8b73-032a2a0c64be\">\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","    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<td>0.00554</td>\n","      <td>0.01109</td>\n","      <td>0.04374</td>\n","      <td>...</td>\n","      <td>0.06545</td>\n","      <td>0.02211</td>\n","      <td>21.033</td>\n","      <td>1</td>\n","      <td>0.414783</td>\n","      <td>0.815285</td>\n","      <td>-4.813031</td>\n","      <td>0.266482</td>\n","      <td>2.301442</td>\n","      <td>0.284654</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>phon_R01_S01_2</td>\n","      <td>122.400</td>\n","      <td>148.650</td>\n","      <td>113.819</td>\n","      <td>0.00968</td>\n","      <td>0.00008</td>\n","      <td>0.00465</td>\n","      <td>0.00696</td>\n","      <td>0.01394</td>\n","      <td>0.06134</td>\n","      <td>...</td>\n","      <td>0.09403</td>\n","      <td>0.01929</td>\n","      <td>19.085</td>\n","      <td>1</td>\n","      <td>0.458359</td>\n","      <td>0.819521</td>\n","      <td>-4.075192</td>\n","      <td>0.335590</td>\n","      <td>2.486855</td>\n","      <td>0.368674</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>phon_R01_S01_3</td>\n","      <td>116.682</td>\n","      <td>131.111</td>\n","      <td>111.555</td>\n","      <td>0.01050</td>\n","      <td>0.00009</td>\n","      <td>0.00544</td>\n","      <td>0.00781</td>\n","      <td>0.01633</td>\n","      <td>0.05233</td>\n","      <td>...</td>\n","      <td>0.08270</td>\n","      <td>0.01309</td>\n","      <td>20.651</td>\n","      <td>1</td>\n","      <td>0.429895</td>\n","      <td>0.825288</td>\n","      <td>-4.443179</td>\n","      <td>0.311173</td>\n","      <td>2.342259</td>\n","      <td>0.332634</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>phon_R01_S01_4</td>\n","      <td>116.676</td>\n","      <td>137.871</td>\n","      <td>111.366</td>\n","      <td>0.00997</td>\n","      <td>0.00009</td>\n","      <td>0.00502</td>\n","      <td>0.00698</td>\n","      <td>0.01505</td>\n","      <td>0.05492</td>\n","      <td>...</td>\n","      <td>0.08771</td>\n","      <td>0.01353</td>\n","      <td>20.644</td>\n","      <td>1</td>\n","      <td>0.434969</td>\n","      <td>0.819235</td>\n","      <td>-4.117501</td>\n","      <td>0.334147</td>\n","      <td>2.405554</td>\n","      <td>0.368975</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>phon_R01_S01_5</td>\n","      <td>116.014</td>\n","      <td>141.781</td>\n","      <td>110.655</td>\n","      <td>0.01284</td>\n","      <td>0.00011</td>\n","      <td>0.00655</td>\n","      <td>0.00908</td>\n","      <td>0.01966</td>\n","      <td>0.06425</td>\n","      <td>...</td>\n","      <td>0.10470</td>\n","      <td>0.01767</td>\n","      <td>19.649</td>\n","      <td>1</td>\n","      <td>0.417356</td>\n","      <td>0.823484</td>\n","      <td>-3.747787</td>\n","      <td>0.234513</td>\n","      <td>2.332180</td>\n","      <td>0.410335</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 24 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" 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  Column            Non-Null Count  Dtype  \n","---  ------            --------------  -----  \n"," 0   name              195 non-null    object \n"," 1   MDVP:Fo(Hz)       195 non-null    float64\n"," 2   MDVP:Fhi(Hz)      195 non-null    float64\n"," 3   MDVP:Flo(Hz)      195 non-null    float64\n"," 4   MDVP:Jitter(%)    195 non-null    float64\n"," 5   MDVP:Jitter(Abs)  195 non-null    float64\n"," 6   MDVP:RAP          195 non-null    float64\n"," 7   MDVP:PPQ          195 non-null    float64\n"," 8   Jitter:DDP        195 non-null    float64\n"," 9   MDVP:Shimmer      195 non-null    float64\n"," 10  MDVP:Shimmer(dB)  195 non-null    float64\n"," 11  Shimmer:APQ3      195 non-null    float64\n"," 12  Shimmer:APQ5      195 non-null    float64\n"," 13  MDVP:APQ          195 non-null    float64\n"," 14  Shimmer:DDA       195 non-null    float64\n"," 15  NHR               195 non-null    float64\n"," 16  HNR               195 non-null    float64\n"," 17  status            195 non-null    int64  \n"," 18  RPDE              195 non-null    float64\n"," 19  DFA               195 non-null    float64\n"," 20  spread1           195 non-null    float64\n"," 21  spread2           195 non-null    float64\n"," 22  D2                195 non-null    float64\n"," 23  PPE               195 non-null    float64\n","dtypes: float64(22), int64(1), object(1)\n","memory usage: 36.7+ KB\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"70rgu_k4ET9F","executionInfo":{"status":"ok","timestamp":1653200307857,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"9d86783c-8f01-468a-a9a0-04552ebc10c7"},"source":["# checking for missing values in each column\n","parkinsons_data.isnull().sum()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["name                0\n","MDVP:Fo(Hz)         0\n","MDVP:Fhi(Hz)        0\n","MDVP:Flo(Hz)        0\n","MDVP:Jitter(%)      0\n","MDVP:Jitter(Abs)    0\n","MDVP:RAP            0\n","MDVP:PPQ            0\n","Jitter:DDP          0\n","MDVP:Shimmer        0\n","MDVP:Shimmer(dB)    0\n","Shimmer:APQ3        0\n","Shimmer:APQ5        0\n","MDVP:APQ            0\n","Shimmer:DDA         0\n","NHR                 0\n","HNR                 0\n","status              0\n","RPDE                0\n","DFA                 0\n","spread1             0\n","spread2             0\n","D2                  0\n","PPE                 0\n","dtype: int64"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"1AxFu0-nEhSA","executionInfo":{"status":"ok","timestamp":1653200308700,"user_tz":-330,"elapsed":853,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"333d5e58-f085-43dc-ccb1-e7fc75084bcf"},"source":["# getting some statistical measures about the data\n","parkinsons_data.describe()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n","count   195.000000    195.000000    195.000000      195.000000   \n","mean    154.228641    197.104918    116.324631        0.006220   \n","std      41.390065     91.491548     43.521413        0.004848   \n","min      88.333000    102.145000     65.476000        0.001680   \n","25%     117.572000    134.862500     84.291000        0.003460   \n","50%     148.790000    175.829000    104.315000        0.004940   \n","75%     182.769000    224.205500    140.018500        0.007365   \n","max     260.105000    592.030000    239.170000        0.033160   \n","\n","       MDVP:Jitter(Abs)    MDVP:RAP    MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n","count        195.000000  195.000000  195.000000  195.000000    195.000000   \n","mean           0.000044    0.003306    0.003446    0.009920      0.029709   \n","std            0.000035    0.002968    0.002759    0.008903      0.018857   \n","min            0.000007    0.000680    0.000920    0.002040      0.009540   \n","25%            0.000020    0.001660    0.001860    0.004985      0.016505   \n","50%            0.000030    0.002500    0.002690    0.007490      0.022970   \n","75%            0.000060    0.003835    0.003955    0.011505      0.037885   \n","max            0.000260    0.021440    0.019580    0.064330      0.119080   \n","\n","       MDVP:Shimmer(dB)  ...  Shimmer:DDA         NHR         HNR      status  \\\n","count        195.000000  ...   195.000000  195.000000  195.000000  195.000000   \n","mean           0.282251  ...     0.046993    0.024847   21.885974    0.753846   \n","std            0.194877  ...     0.030459    0.040418    4.425764    0.431878   \n","min            0.085000  ...     0.013640    0.000650    8.441000    0.000000   \n","25%            0.148500  ...     0.024735    0.005925   19.198000    1.000000   \n","50%            0.221000  ...     0.038360    0.011660   22.085000    1.000000   \n","75%            0.350000  ...     0.060795    0.025640   25.075500    1.000000   \n","max            1.302000  ...     0.169420    0.314820   33.047000    1.000000   \n","\n","             RPDE         DFA     spread1     spread2          D2         PPE  \n","count  195.000000  195.000000  195.000000  195.000000  195.000000  195.000000  \n","mean     0.498536    0.718099   -5.684397    0.226510    2.381826    0.206552  \n","std      0.103942    0.055336    1.090208    0.083406    0.382799    0.090119  \n","min      0.256570    0.574282   -7.964984    0.006274    1.423287    0.044539  \n","25%      0.421306    0.674758   -6.450096    0.174351    2.099125    0.137451  \n","50%      0.495954    0.722254   -5.720868    0.218885    2.361532    0.194052  \n","75%      0.587562    0.761881   -5.046192    0.279234    2.636456    0.252980  \n","max      0.685151    0.825288   -2.434031    0.450493    3.671155    0.527367  \n","\n","[8 rows x 23 columns]"],"text/html":["\n","  <div id=\"df-b83bb5a5-5785-498f-b146-d62b868ce2a6\">\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>MDVP:Fo(Hz)</th>\n","      <th>MDVP:Fhi(Hz)</th>\n","      <th>MDVP:Flo(Hz)</th>\n","      <th>MDVP:Jitter(%)</th>\n","      <th>MDVP:Jitter(Abs)</th>\n","      <th>MDVP:RAP</th>\n","      <th>MDVP:PPQ</th>\n","      <th>Jitter:DDP</th>\n","      <th>MDVP:Shimmer</th>\n","      <th>MDVP:Shimmer(dB)</th>\n","      <th>...</th>\n","      <th>Shimmer:DDA</th>\n","      <th>NHR</th>\n","      <th>HNR</th>\n","      <th>status</th>\n","      <th>RPDE</th>\n","      <th>DFA</th>\n","      <th>spread1</th>\n","      <th>spread2</th>\n","      <th>D2</th>\n","      <th>PPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>...</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","      <td>195.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>154.228641</td>\n","      <td>197.104918</td>\n","      <td>116.324631</td>\n","      <td>0.006220</td>\n","      <td>0.000044</td>\n","      <td>0.003306</td>\n","      <td>0.003446</td>\n","      <td>0.009920</td>\n","      <td>0.029709</td>\n","      <td>0.282251</td>\n","      <td>...</td>\n","      <td>0.046993</td>\n","      <td>0.024847</td>\n","      <td>21.885974</td>\n","      <td>0.753846</td>\n","      <td>0.498536</td>\n","      <td>0.718099</td>\n","      <td>-5.684397</td>\n","      <td>0.226510</td>\n","      <td>2.381826</td>\n","      <td>0.206552</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>41.390065</td>\n","      <td>91.491548</td>\n","      <td>43.521413</td>\n","      <td>0.004848</td>\n","      <td>0.000035</td>\n","      <td>0.002968</td>\n","      <td>0.002759</td>\n","      <td>0.008903</td>\n","      <td>0.018857</td>\n","      <td>0.194877</td>\n","      <td>...</td>\n","      <td>0.030459</td>\n","      <td>0.040418</td>\n","      <td>4.425764</td>\n","      <td>0.431878</td>\n","      <td>0.103942</td>\n","      <td>0.055336</td>\n","      <td>1.090208</td>\n","      <td>0.083406</td>\n","      <td>0.382799</td>\n","      <td>0.090119</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>88.333000</td>\n","      <td>102.145000</td>\n","      <td>65.476000</td>\n","      <td>0.001680</td>\n","      <td>0.000007</td>\n","      <td>0.000680</td>\n","      <td>0.000920</td>\n","      <td>0.002040</td>\n","      <td>0.009540</td>\n","      <td>0.085000</td>\n","      <td>...</td>\n","      <td>0.013640</td>\n","      <td>0.000650</td>\n","      <td>8.441000</td>\n","      <td>0.000000</td>\n","      <td>0.256570</td>\n","      <td>0.574282</td>\n","      <td>-7.964984</td>\n","      <td>0.006274</td>\n","      <td>1.423287</td>\n","      <td>0.044539</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>117.572000</td>\n","      <td>134.862500</td>\n","      <td>84.291000</td>\n","      <td>0.003460</td>\n","      <td>0.000020</td>\n","      <td>0.001660</td>\n","      <td>0.001860</td>\n","      <td>0.004985</td>\n","      <td>0.016505</td>\n","      <td>0.148500</td>\n","      <td>...</td>\n","      <td>0.024735</td>\n","      <td>0.005925</td>\n","      <td>19.198000</td>\n","      <td>1.000000</td>\n","      <td>0.421306</td>\n","      <td>0.674758</td>\n","      <td>-6.450096</td>\n","      <td>0.174351</td>\n","      <td>2.099125</td>\n","      <td>0.137451</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>148.790000</td>\n","      <td>175.829000</td>\n","      <td>104.315000</td>\n","      <td>0.004940</td>\n","      <td>0.000030</td>\n","      <td>0.002500</td>\n","      <td>0.002690</td>\n","      <td>0.007490</td>\n","      <td>0.022970</td>\n","      <td>0.221000</td>\n","      <td>...</td>\n","      <td>0.038360</td>\n","      <td>0.011660</td>\n","      <td>22.085000</td>\n","      <td>1.000000</td>\n","      <td>0.495954</td>\n","      <td>0.722254</td>\n","      <td>-5.720868</td>\n","      <td>0.218885</td>\n","      <td>2.361532</td>\n","      <td>0.194052</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>182.769000</td>\n","      <td>224.205500</td>\n","      <td>140.018500</td>\n","      <td>0.007365</td>\n","      <td>0.000060</td>\n","      <td>0.003835</td>\n","      <td>0.003955</td>\n","      <td>0.011505</td>\n","      <td>0.037885</td>\n","      <td>0.350000</td>\n","      <td>...</td>\n","      <td>0.060795</td>\n","      <td>0.025640</td>\n","      <td>25.075500</td>\n","      <td>1.000000</td>\n","      <td>0.587562</td>\n","      <td>0.761881</td>\n","      <td>-5.046192</td>\n","      <td>0.279234</td>\n","      <td>2.636456</td>\n","      <td>0.252980</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>260.105000</td>\n","      <td>592.030000</td>\n","      <td>239.170000</td>\n","      <td>0.033160</td>\n","      <td>0.000260</td>\n","      <td>0.021440</td>\n","      <td>0.019580</td>\n","      <td>0.064330</td>\n","      <td>0.119080</td>\n","      <td>1.302000</td>\n","      <td>...</td>\n","      <td>0.169420</td>\n","      <td>0.314820</td>\n","      <td>33.047000</td>\n","      <td>1.000000</td>\n","      <td>0.685151</td>\n","      <td>0.825288</td>\n","      <td>-2.434031</td>\n","      <td>0.450493</td>\n","      <td>3.671155</td>\n","      <td>0.527367</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8 rows × 23 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" 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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":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3O8AclzwExyH","executionInfo":{"status":"ok","timestamp":1653200308701,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"6a330028-c2a3-431a-f1cb-b529137cdcc7"},"source":["# distribution of target Variable\n","parkinsons_data['status'].value_counts()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    147\n","0     48\n","Name: status, dtype: int64"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"L1srlxtEFYfN"},"source":["1  --> Parkinson's Positive\n","\n","0 --> Healthy\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"zUrPan7CFTMq","executionInfo":{"status":"ok","timestamp":1653200308702,"user_tz":-330,"elapsed":18,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"9addca6f-f25f-4cde-aa11-266fbece8b9f"},"source":["# grouping the data bas3ed on the target variable\n","parkinsons_data.groupby('status').mean()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n","status                                                            \n","0        181.937771    223.636750    145.207292        0.003866   \n","1        145.180762    188.441463    106.893558        0.006989   \n","\n","        MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n","status                                                                   \n","0               0.000023  0.001925  0.002056    0.005776      0.017615   \n","1               0.000051  0.003757  0.003900    0.011273      0.033658   \n","\n","        MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA       NHR        HNR  \\\n","status                    ...                                               \n","0               0.162958  ...  0.013305     0.028511  0.011483  24.678750   \n","1               0.321204  ...  0.027600     0.053027  0.029211  20.974048   \n","\n","            RPDE       DFA   spread1   spread2        D2       PPE  \n","status                                                              \n","0       0.442552  0.695716 -6.759264  0.160292  2.154491  0.123017  \n","1       0.516816  0.725408 -5.333420  0.248133  2.456058  0.233828  \n","\n","[2 rows x 22 columns]"],"text/html":["\n","  <div id=\"df-2223dde4-4970-4602-b034-347b3317c577\">\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","        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<th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>181.937771</td>\n","      <td>223.636750</td>\n","      <td>145.207292</td>\n","      <td>0.003866</td>\n","      <td>0.000023</td>\n","      <td>0.001925</td>\n","      <td>0.002056</td>\n","      <td>0.005776</td>\n","      <td>0.017615</td>\n","      <td>0.162958</td>\n","      <td>...</td>\n","      <td>0.013305</td>\n","      <td>0.028511</td>\n","      <td>0.011483</td>\n","      <td>24.678750</td>\n","      <td>0.442552</td>\n","      <td>0.695716</td>\n","      <td>-6.759264</td>\n","      <td>0.160292</td>\n","      <td>2.154491</td>\n","      <td>0.123017</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>145.180762</td>\n","      <td>188.441463</td>\n","      <td>106.893558</td>\n","      <td>0.006989</td>\n","      <td>0.000051</td>\n","      <td>0.003757</td>\n","      <td>0.003900</td>\n","      <td>0.011273</td>\n","      <td>0.033658</td>\n","      <td>0.321204</td>\n","      <td>...</td>\n","      <td>0.027600</td>\n","      <td>0.053027</td>\n","      <td>0.029211</td>\n","      <td>20.974048</td>\n","      <td>0.516816</td>\n","      <td>0.725408</td>\n","      <td>-5.333420</td>\n","      <td>0.248133</td>\n","      <td>2.456058</td>\n","      <td>0.233828</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 22 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2223dde4-4970-4602-b034-347b3317c577')\"\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","       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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":9}]},{"cell_type":"markdown","metadata":{"id":"8RY6c0waGSs7"},"source":["Data Pre-Processing"]},{"cell_type":"markdown","metadata":{"id":"We7sRYu7Gc4q"},"source":["Separating the features & Target"]},{"cell_type":"code","metadata":{"id":"UAcz8jFnFuzH","executionInfo":{"status":"ok","timestamp":1653200308702,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X = parkinsons_data.drop(columns=['name','status'], axis=1)\n","Y = parkinsons_data['status']"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"guRof_8WG1Yn","executionInfo":{"status":"ok","timestamp":1653200308702,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"531b55ec-5615-47e6-dde6-51295bfe8945"},"source":["print(X)"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["     MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n","0        119.992       157.302        74.997         0.00784   \n","1        122.400       148.650       113.819         0.00968   \n","2        116.682       131.111       111.555         0.01050   \n","3        116.676       137.871       111.366         0.00997   \n","4        116.014       141.781       110.655         0.01284   \n","..           ...           ...           ...             ...   \n","190      174.188       230.978        94.261         0.00459   \n","191      209.516       253.017        89.488         0.00564   \n","192      174.688       240.005        74.287         0.01360   \n","193      198.764       396.961        74.904         0.00740   \n","194      214.289       260.277        77.973         0.00567   \n","\n","     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n","0             0.00007   0.00370   0.00554     0.01109       0.04374   \n","1             0.00008   0.00465   0.00696     0.01394       0.06134   \n","2             0.00009   0.00544   0.00781     0.01633       0.05233   \n","3             0.00009   0.00502   0.00698     0.01505       0.05492   \n","4             0.00011   0.00655   0.00908     0.01966       0.06425   \n","..                ...       ...       ...         ...           ...   \n","190           0.00003   0.00263   0.00259     0.00790       0.04087   \n","191           0.00003   0.00331   0.00292     0.00994       0.02751   \n","192           0.00008   0.00624   0.00564     0.01873       0.02308   \n","193           0.00004   0.00370   0.00390     0.01109       0.02296   \n","194           0.00003   0.00295   0.00317     0.00885       0.01884   \n","\n","     MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA      NHR     HNR      RPDE  \\\n","0               0.426  ...   0.02971      0.06545  0.02211  21.033  0.414783   \n","1               0.626  ...   0.04368      0.09403  0.01929  19.085  0.458359   \n","2               0.482  ...   0.03590      0.08270  0.01309  20.651  0.429895   \n","3               0.517  ...   0.03772      0.08771  0.01353  20.644  0.434969   \n","4               0.584  ...   0.04465      0.10470  0.01767  19.649  0.417356   \n","..                ...  ...       ...          ...      ...     ...       ...   \n","190             0.405  ...   0.02745      0.07008  0.02764  19.517  0.448439   \n","191             0.263  ...   0.01879      0.04812  0.01810  19.147  0.431674   \n","192             0.256  ...   0.01667      0.03804  0.10715  17.883  0.407567   \n","193             0.241  ...   0.01588      0.03794  0.07223  19.020  0.451221   \n","194             0.190  ...   0.01373      0.03078  0.04398  21.209  0.462803   \n","\n","          DFA   spread1   spread2        D2       PPE  \n","0    0.815285 -4.813031  0.266482  2.301442  0.284654  \n","1    0.819521 -4.075192  0.335590  2.486855  0.368674  \n","2    0.825288 -4.443179  0.311173  2.342259  0.332634  \n","3    0.819235 -4.117501  0.334147  2.405554  0.368975  \n","4    0.823484 -3.747787  0.234513  2.332180  0.410335  \n","..        ...       ...       ...       ...       ...  \n","190  0.657899 -6.538586  0.121952  2.657476  0.133050  \n","191  0.683244 -6.195325  0.129303  2.784312  0.168895  \n","192  0.655683 -6.787197  0.158453  2.679772  0.131728  \n","193  0.643956 -6.744577  0.207454  2.138608  0.123306  \n","194  0.664357 -5.724056  0.190667  2.555477  0.148569  \n","\n","[195 rows x 22 columns]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xSNrvkJoG3cY","executionInfo":{"status":"ok","timestamp":1653200308703,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"db156ede-5d9e-4ab4-de6d-ead138a71faf"},"source":["print(Y)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["0      1\n","1      1\n","2      1\n","3      1\n","4      1\n","      ..\n","190    0\n","191    0\n","192    0\n","193    0\n","194    0\n","Name: status, Length: 195, dtype: int64\n"]}]},{"cell_type":"markdown","metadata":{"id":"WDeqEaaHHBAS"},"source":["Splitting the data to training data & Test data"]},{"cell_type":"code","metadata":{"id":"4c6nrCiVG6NB","executionInfo":{"status":"ok","timestamp":1653200309503,"user_tz":-330,"elapsed":810,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6OqUka96H35c","executionInfo":{"status":"ok","timestamp":1653200309504,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"47eb1e86-5aa5-41f1-deb2-e02d9d2bffe7"},"source":["print(X.shape, X_train.shape, X_test.shape)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["(195, 22) (156, 22) (39, 22)\n"]}]},{"cell_type":"markdown","metadata":{"id":"QIOAtx35JUMg"},"source":["Model Training"]},{"cell_type":"markdown","metadata":{"id":"fWlsaBNuJV5g"},"source":["Support Vector Machine Model"]},{"cell_type":"code","metadata":{"id":"IDInA1u5JCZ9","executionInfo":{"status":"ok","timestamp":1653200309504,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["model = svm.SVC(kernel='linear')"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F01DNpqWKmaW","executionInfo":{"status":"ok","timestamp":1653200309505,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"a681ba26-aa79-4b09-a0a3-ddd653726c52"},"source":["# training the SVM model with training data\n","model.fit(X_train, Y_train)"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(kernel='linear')"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"1z_-nZfuLJrH"},"source":["Model Evaluation"]},{"cell_type":"markdown","metadata":{"id":"Rj3XAnF8LMF4"},"source":["Accuracy Score"]},{"cell_type":"code","metadata":{"id":"5LwxNgnqK1Za","executionInfo":{"status":"ok","timestamp":1653200309505,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy score on training data\n","X_train_prediction = model.predict(X_train)\n","training_data_accuracy = accuracy_score(Y_train, X_train_prediction)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-dS9tcGdLm41","executionInfo":{"status":"ok","timestamp":1653200309506,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"bbc0b0cd-ed16-4430-f137-8fdaf1f2183d"},"source":["print('Accuracy score of training data : ', training_data_accuracy)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy score of training data :  0.8717948717948718\n"]}]},{"cell_type":"code","metadata":{"id":"rNUO2uHmLtjY","executionInfo":{"status":"ok","timestamp":1653200309506,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy score on training data\n","X_test_prediction = model.predict(X_test)\n","test_data_accuracy = accuracy_score(Y_test, X_test_prediction)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BsF3UnQ2L_aR","executionInfo":{"status":"ok","timestamp":1653200309506,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"80347c36-1481-4ba3-9cf3-7ed635ca5a85"},"source":["print('Accuracy score of test data : ', test_data_accuracy)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy score of test data :  0.8717948717948718\n"]}]},{"cell_type":"markdown","metadata":{"id":"QlR4JG4YMfOR"},"source":["Building a Predictive System"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w0FjSoO1MGBU","executionInfo":{"status":"ok","timestamp":1653200309507,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"a4715754-198f-4927-9df3-e8a104a9b154"},"source":["input_data = (197.07600,206.89600,192.05500,0.00289,0.00001,0.00166,0.00168,0.00498,0.01098,0.09700,0.00563,0.00680,0.00802,0.01689,0.00339,26.77500,0.422229,0.741367,-7.348300,0.177551,1.743867,0.085569)\n","\n","# changing input data to a numpy array\n","input_data_as_numpy_array = np.asarray(input_data)\n","\n","# reshape the numpy array\n","input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n","\n","prediction = model.predict(input_data_reshaped)\n","print(prediction)\n","\n","\n","if (prediction[0] == 0):\n","  print(\"The Person does not have Parkinsons Disease\")\n","\n","else:\n","  print(\"The Person has Parkinsons\")\n"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["[0]\n","The Person does not have Parkinsons Disease\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n","  \"X does not have valid feature names, but\"\n"]}]},{"cell_type":"markdown","metadata":{"id":"FCHCMHpshHU4"},"source":["Saving the trained model"]},{"cell_type":"code","metadata":{"id":"cdmTOR4MhHCB","executionInfo":{"status":"ok","timestamp":1653200309507,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import pickle"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"4gN09lokhKuZ","executionInfo":{"status":"ok","timestamp":1653200309508,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["filename = 'parkinsons_model.sav'\n","pickle.dump(model, open(filename, 'wb'))"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"IKW4D5CqhP5X","executionInfo":{"status":"ok","timestamp":1653200309510,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the saved model\n","loaded_model = pickle.load(open('parkinsons_model.sav', 'rb'))"],"execution_count":24,"outputs":[]},{"cell_type":"code","source":["for column in X.columns:\n","  print(column)"],"metadata":{"id":"m8FO1U8hRVm_","executionInfo":{"status":"ok","timestamp":1653200309511,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"079be68d-fb39-4544-9100-c6388b397091","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["MDVP:Fo(Hz)\n","MDVP:Fhi(Hz)\n","MDVP:Flo(Hz)\n","MDVP:Jitter(%)\n","MDVP:Jitter(Abs)\n","MDVP:RAP\n","MDVP:PPQ\n","Jitter:DDP\n","MDVP:Shimmer\n","MDVP:Shimmer(dB)\n","Shimmer:APQ3\n","Shimmer:APQ5\n","MDVP:APQ\n","Shimmer:DDA\n","NHR\n","HNR\n","RPDE\n","DFA\n","spread1\n","spread2\n","D2\n","PPE\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"JPyuHFeDRXZU","executionInfo":{"status":"ok","timestamp":1653200309512,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"execution_count":25,"outputs":[]}]}

colab_files_to_train_models/Multiple disease prediction system - diabetes.ipynb

@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Multiple disease prediction system - diabetes.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyNUMtWtqiQOs+bSuSretvw5"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"LnPbntVRnfvV"},"source":["Importing the Dependencies"]},{"cell_type":"code","metadata":{"id":"-71UtHzNVWjB","executionInfo":{"status":"ok","timestamp":1653200161038,"user_tz":-330,"elapsed":1362,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn import svm\n","from sklearn.metrics import accuracy_score"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bmfOfG8joBBy"},"source":["Data Collection and Analysis\n","\n","PIMA Diabetes Dataset"]},{"cell_type":"code","metadata":{"id":"Xpw6Mj_pn_TL","executionInfo":{"status":"ok","timestamp":1653200161039,"user_tz":-330,"elapsed":37,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the diabetes dataset to a pandas DataFrame\n","diabetes_dataset = pd.read_csv('/content/diabetes.csv') "],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"-tjO09ncovoh","executionInfo":{"status":"ok","timestamp":1653200161040,"user_tz":-330,"elapsed":36,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"f4faaf8a-3fc8-413a-bbd4-bb272589a73c"},"source":["# printing the first 5 rows of the dataset\n","diabetes_dataset.head()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n","0            6      148             72             35        0  33.6   \n","1            1       85             66             29        0  26.6   \n","2            8      183             64              0        0  23.3   \n","3            1       89             66             23       94  28.1   \n","4            0      137             40             35      168  43.1   \n","\n","   DiabetesPedigreeFunction  Age  Outcome  \n","0                     0.627   50        1  \n","1                     0.351   31        0  \n","2                     0.672   32        1  \n","3                     0.167   21        0  \n","4                     2.288   33        1  "],"text/html":["\n","  <div id=\"df-543bb76d-9958-491b-8063-13990fcca102\">\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","        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<th>2</th>\n","      <td>8</td>\n","      <td>183</td>\n","      <td>64</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>23.3</td>\n","      <td>0.672</td>\n","      <td>32</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>89</td>\n","      <td>66</td>\n","      <td>23</td>\n","      <td>94</td>\n","      <td>28.1</td>\n","      <td>0.167</td>\n","      <td>21</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>137</td>\n","      <td>40</td>\n","      <td>35</td>\n","      <td>168</td>\n","      <td>43.1</td>\n","      <td>2.288</td>\n","      <td>33</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-543bb76d-9958-491b-8063-13990fcca102')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              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dataset\n","diabetes_dataset.shape"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(768, 9)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"3NDJOlrEpmoL","executionInfo":{"status":"ok","timestamp":1653200161041,"user_tz":-330,"elapsed":26,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"1a166b9c-0ec1-42dc-e0f4-32924aa1ab65"},"source":["# getting the statistical measures of the data\n","diabetes_dataset.describe()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n","count   768.000000  768.000000     768.000000     768.000000  768.000000   \n","mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n","std       3.369578   31.972618      19.355807      15.952218  115.244002   \n","min       0.000000    0.000000       0.000000       0.000000    0.000000   \n","25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n","50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n","75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n","max      17.000000  199.000000     122.000000      99.000000  846.000000   \n","\n","              BMI  DiabetesPedigreeFunction         Age     Outcome  \n","count  768.000000                768.000000  768.000000  768.000000  \n","mean    31.992578                  0.471876   33.240885    0.348958  \n","std      7.884160                  0.331329   11.760232    0.476951  \n","min      0.000000                  0.078000   21.000000    0.000000  \n","25%     27.300000                  0.243750   24.000000    0.000000  \n","50%     32.000000                  0.372500   29.000000    0.000000  \n","75%     36.600000                  0.626250   41.000000    1.000000  \n","max     67.100000                  2.420000   81.000000    1.000000  "],"text/html":["\n","  <div id=\"df-e3ca105c-2704-4b75-87c3-2eac2e0e898f\">\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>Pregnancies</th>\n","      <th>Glucose</th>\n","      <th>BloodPressure</th>\n","      <th>SkinThickness</th>\n","      <th>Insulin</th>\n","      <th>BMI</th>\n","      <th>DiabetesPedigreeFunction</th>\n","      <th>Age</th>\n","      <th>Outcome</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.845052</td>\n","      <td>120.894531</td>\n","      <td>69.105469</td>\n","      <td>20.536458</td>\n","      <td>79.799479</td>\n","      <td>31.992578</td>\n","      <td>0.471876</td>\n","      <td>33.240885</td>\n","      <td>0.348958</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3.369578</td>\n","      <td>31.972618</td>\n","      <td>19.355807</td>\n","      <td>15.952218</td>\n","      <td>115.244002</td>\n","      <td>7.884160</td>\n","      <td>0.331329</td>\n","      <td>11.760232</td>\n","      <td>0.476951</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.078000</td>\n","      <td>21.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.000000</td>\n","      <td>99.000000</td>\n","      <td>62.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>27.300000</td>\n","      <td>0.243750</td>\n","      <td>24.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>3.000000</td>\n","      <td>117.000000</td>\n","      <td>72.000000</td>\n","      <td>23.000000</td>\n","      <td>30.500000</td>\n","      <td>32.000000</td>\n","      <td>0.372500</td>\n","      <td>29.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>6.000000</td>\n","      <td>140.250000</td>\n","      <td>80.000000</td>\n","      <td>32.000000</td>\n","      <td>127.250000</td>\n","      <td>36.600000</td>\n","      <td>0.626250</td>\n","      <td>41.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>17.000000</td>\n","      <td>199.000000</td>\n","      <td>122.000000</td>\n","      <td>99.000000</td>\n","      <td>846.000000</td>\n","      <td>67.100000</td>\n","      <td>2.420000</td>\n","      <td>81.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3ca105c-2704-4b75-87c3-2eac2e0e898f')\"\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 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                                                                   \n","0           3.298000  109.980000      68.184000      19.664000   68.792000   \n","1           4.865672  141.257463      70.824627      22.164179  100.335821   \n","\n","               BMI  DiabetesPedigreeFunction        Age  \n","Outcome                                                  \n","0        30.304200                  0.429734  31.190000  \n","1        35.142537                  0.550500  37.067164  "],"text/html":["\n","  <div id=\"df-d6444b5f-72b8-4e7e-b7a2-6fc0efdc7816\">\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: 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<td>35.142537</td>\n","      <td>0.550500</td>\n","      <td>37.067164</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6444b5f-72b8-4e7e-b7a2-6fc0efdc7816')\"\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 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diabetes_dataset['Outcome']"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3eiRW9M9raMm","executionInfo":{"status":"ok","timestamp":1653200161046,"user_tz":-330,"elapsed":26,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"107aefe8-a40e-4a8c-efa4-e6a4929b8343"},"source":["print(X)"],"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n","0              6      148             72             35        0  33.6   \n","1              1       85             66             29        0  26.6   \n","2              8      183             64              0        0  23.3   \n","3              1       89             66             23       94  28.1   \n","4              0      137             40             35      168  43.1   \n","..           ...      ...            ...            ...      ...   ...   \n","763           10      101             76             48      180  32.9   \n","764            2      122             70             27        0  36.8   \n","765            5      121             72             23      112  26.2   \n","766            1      126             60              0        0  30.1   \n","767            1       93             70             31        0  30.4   \n","\n","     DiabetesPedigreeFunction  Age  \n","0                       0.627   50  \n","1                       0.351   31  \n","2                       0.672   32  \n","3                       0.167   21  \n","4                       2.288   33  \n","..                        ...  ...  \n","763                     0.171   63  \n","764                     0.340   27  \n","765                     0.245   30  \n","766                     0.349   47  \n","767                     0.315   23  \n","\n","[768 rows x 8 columns]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AoxgTJAMrcCl","executionInfo":{"status":"ok","timestamp":1653200161047,"user_tz":-330,"elapsed":25,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"afb40f51-fd9c-43ff-c51f-7b49a7f9b152"},"source":["print(Y)"],"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["0      1\n","1      0\n","2      1\n","3      0\n","4      1\n","      ..\n","763    0\n","764    0\n","765    0\n","766    1\n","767    0\n","Name: Outcome, Length: 768, dtype: int64\n"]}]},{"cell_type":"markdown","metadata":{"id":"gHciEFkxsoQP"},"source":["Train Test Split"]},{"cell_type":"code","metadata":{"id":"AEfKGj_yslvD","executionInfo":{"status":"ok","timestamp":1653200161047,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DR05T-o0t3FQ","executionInfo":{"status":"ok","timestamp":1653200161048,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"1e35e84b-b1ac-4cf3-c2af-31eb3bf2e71c"},"source":["print(X.shape, X_train.shape, X_test.shape)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["(768, 8) (614, 8) (154, 8)\n"]}]},{"cell_type":"markdown","metadata":{"id":"ElJ3tkOtuC_n"},"source":["Training the Model"]},{"cell_type":"code","metadata":{"id":"5szLWHlNt9xc","executionInfo":{"status":"ok","timestamp":1653200161048,"user_tz":-330,"elapsed":19,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["classifier = svm.SVC(kernel='linear')"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ncJWY_7suPAb","executionInfo":{"status":"ok","timestamp":1653200163540,"user_tz":-330,"elapsed":2510,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"00260e64-02fd-426f-d274-717bbf9318d6"},"source":["#training the support vector Machine Classifier\n","classifier.fit(X_train, Y_train)"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(kernel='linear')"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"UV4-CAfquiyP"},"source":["Model Evaluation"]},{"cell_type":"markdown","metadata":{"id":"yhAjGPJWunXa"},"source":["Accuracy Score"]},{"cell_type":"code","metadata":{"id":"fJLEPQK7ueXp","executionInfo":{"status":"ok","timestamp":1653200163541,"user_tz":-330,"elapsed":27,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy score on the training data\n","X_train_prediction = classifier.predict(X_train)\n","training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mmJ22qhVvNwj","executionInfo":{"status":"ok","timestamp":1653200163541,"user_tz":-330,"elapsed":25,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"ec01e94f-dbed-4286-9413-a05dab7eb7bc"},"source":["print('Accuracy score of the training data : ', training_data_accuracy)"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy score of the training data :  0.7833876221498371\n"]}]},{"cell_type":"code","metadata":{"id":"G2CICFMEvcCl","executionInfo":{"status":"ok","timestamp":1653200163542,"user_tz":-330,"elapsed":24,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy score on the test data\n","X_test_prediction = classifier.predict(X_test)\n","test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i2GcW_t_vz7C","executionInfo":{"status":"ok","timestamp":1653200163542,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"638e2f46-36ec-471a-b9f7-ac7e319e5fea"},"source":["print('Accuracy score of the test data : ', test_data_accuracy)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy score of the test data :  0.7727272727272727\n"]}]},{"cell_type":"markdown","metadata":{"id":"gq8ZX1xpwPF5"},"source":["Making a Predictive System"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U-ULRe4yv5tH","executionInfo":{"status":"ok","timestamp":1653200163543,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"6d829789-0c86-469d-ec6a-f20276f0a3ed"},"source":["input_data = (5,166,72,19,175,25.8,0.587,51)\n","\n","# changing the input_data to numpy array\n","input_data_as_numpy_array = np.asarray(input_data)\n","\n","# reshape the array as we are predicting for one instance\n","input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n","\n","prediction = classifier.predict(input_data_reshaped)\n","print(prediction)\n","\n","if (prediction[0] == 0):\n","  print('The person is not diabetic')\n","else:\n","  print('The person is diabetic')"],"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["[1]\n","The person is diabetic\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n","  \"X does not have valid feature names, but\"\n"]}]},{"cell_type":"markdown","metadata":{"id":"FCHCMHpshHU4"},"source":["Saving the trained model"]},{"cell_type":"code","metadata":{"id":"cdmTOR4MhHCB","executionInfo":{"status":"ok","timestamp":1653200163543,"user_tz":-330,"elapsed":18,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import pickle"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"4gN09lokhKuZ","executionInfo":{"status":"ok","timestamp":1653200163543,"user_tz":-330,"elapsed":17,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["filename = 'diabetes_model.sav'\n","pickle.dump(classifier, open(filename, 'wb'))"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"IKW4D5CqhP5X","executionInfo":{"status":"ok","timestamp":1653200163544,"user_tz":-330,"elapsed":18,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the saved model\n","loaded_model = pickle.load(open('diabetes_model.sav', 'rb'))"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"exbg9-VWiHRx","executionInfo":{"status":"ok","timestamp":1653200163544,"user_tz":-330,"elapsed":17,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"e9d0e88b-67b8-457a-a252-22633cdc382e"},"source":["input_data = (5,166,72,19,175,25.8,0.587,51)\n","\n","# changing the input_data to numpy array\n","input_data_as_numpy_array = np.asarray(input_data)\n","\n","# reshape the array as we are predicting for one instance\n","input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n","\n","prediction = loaded_model.predict(input_data_reshaped)\n","print(prediction)\n","\n","if (prediction[0] == 0):\n","  print('The person is not diabetic')\n","else:\n","  print('The person is diabetic')"],"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["[1]\n","The person is diabetic\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n","  \"X does not have valid feature names, but\"\n"]}]},{"cell_type":"code","source":["for column in X.columns:\n","  print(column)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IP-TYuEFOTF4","executionInfo":{"status":"ok","timestamp":1653200163545,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"508fa3ac-ee67-4361-e558-d6b22b9c2c2c"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Pregnancies\n","Glucose\n","BloodPressure\n","SkinThickness\n","Insulin\n","BMI\n","DiabetesPedigreeFunction\n","Age\n"]}]}]}

colab_files_to_train_models/Multiple disease prediction system - heart.ipynb

@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Multiple disease prediction system - heart.ipynb","provenance":[],"authorship_tag":"ABX9TyNkEJ4hIKtyX260ZMlxNVm4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"aTb-9TFFqprC"},"source":["Importing the Dependencies"]},{"cell_type":"code","metadata":{"id":"3q9U3S_whh3-","executionInfo":{"status":"ok","timestamp":1653200233515,"user_tz":-330,"elapsed":1659,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"egMd5zeurTMR"},"source":["Data Collection and Processing"]},{"cell_type":"code","metadata":{"id":"0q-3-LkQrREV","executionInfo":{"status":"ok","timestamp":1653200233516,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the csv data to a Pandas DataFrame\n","heart_data = pd.read_csv('/content/heart.csv')"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"M8dQxSTqriWD","executionInfo":{"status":"ok","timestamp":1653200233516,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"0ab48855-8403-4103-c7c8-e307c07da055"},"source":["# print first 5 rows of the dataset\n","heart_data.head()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n","0   63    1   3       145   233    1        0      150      0      2.3      0   \n","1   37    1   2       130   250    0        1      187      0      3.5      0   \n","2   41    0   1       130   204    0        0      172      0      1.4      2   \n","3   56    1   1       120   236    0        1      178      0      0.8      2   \n","4   57    0   0       120   354    0        1      163      1      0.6      2   \n","\n","   ca  thal  target  \n","0   0     1       1  \n","1   0     2       1  \n","2   0     2       1  \n","3   0     2       1  \n","4   0     2       1  "],"text/html":["\n","  <div id=\"df-c22c32f4-9008-4881-915a-af9a1cc44c73\">\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>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>145</td>\n","      <td>233</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>150</td>\n","      <td>0</td>\n","      <td>2.3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>37</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>130</td>\n","      <td>250</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>187</td>\n","      <td>0</td>\n","      <td>3.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>204</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>172</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>56</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>120</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>178</td>\n","      <td>0</td>\n","      <td>0.8</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>354</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>163</td>\n","      <td>1</td>\n","      <td>0.6</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c22c32f4-9008-4881-915a-af9a1cc44c73')\"\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 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dataset\n","heart_data.tail()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","298   57    0   0       140   241    0        1      123      1      0.2   \n","299   45    1   3       110   264    0        1      132      0      1.2   \n","300   68    1   0       144   193    1        1      141      0      3.4   \n","301   57    1   0       130   131    0        1      115      1      1.2   \n","302   57    0   1       130   236    0        0      174      0      0.0   \n","\n","     slope  ca  thal  target  \n","298      1   0     3       0  \n","299      1   0     3       0  \n","300      1   2     3       0  \n","301      1   1     3       0  \n","302      1   1     2       0  "],"text/html":["\n","  <div id=\"df-8a23c18a-4205-4dce-9342-e086c7334c7b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr 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dataset\n","heart_data.shape"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(303, 14)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7_xTcw1Sr6aJ","executionInfo":{"status":"ok","timestamp":1653200234315,"user_tz":-330,"elapsed":26,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"3dfcf6cf-f814-4b3b-f8be-57e2ea6ea7b2"},"source":["# getting some info about the data\n","heart_data.info()"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 303 entries, 0 to 302\n","Data columns (total 14 columns):\n"," #   Column    Non-Null Count  Dtype  \n","---  ------    --------------  -----  \n"," 0   age       303 non-null    int64  \n"," 1   sex       303 non-null    int64  \n"," 2   cp        303 non-null    int64  \n"," 3   trestbps  303 non-null    int64  \n"," 4   chol      303 non-null    int64  \n"," 5   fbs       303 non-null    int64  \n"," 6   restecg   303 non-null    int64  \n"," 7   thalach   303 non-null    int64  \n"," 8   exang     303 non-null    int64  \n"," 9   oldpeak   303 non-null    float64\n"," 10  slope     303 non-null    int64  \n"," 11  ca        303 non-null    int64  \n"," 12  thal      303 non-null    int64  \n"," 13  target    303 non-null    int64  \n","dtypes: float64(1), int64(13)\n","memory usage: 33.3 KB\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GjHtW31rsGlb","executionInfo":{"status":"ok","timestamp":1653200234316,"user_tz":-330,"elapsed":25,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"062af8fa-b836-480d-93dd-b84f335f0839"},"source":["# checking for missing values\n","heart_data.isnull().sum()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age         0\n","sex         0\n","cp          0\n","trestbps    0\n","chol        0\n","fbs         0\n","restecg     0\n","thalach     0\n","exang       0\n","oldpeak     0\n","slope       0\n","ca          0\n","thal        0\n","target      0\n","dtype: int64"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":364},"id":"OHmcP7DJsSEP","executionInfo":{"status":"ok","timestamp":1653200234317,"user_tz":-330,"elapsed":24,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"b08b05fa-7f04-45bb-8f9b-c676a77834c3"},"source":["# statistical measures about the data\n","heart_data.describe()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              age         sex          cp    trestbps        chol         fbs  \\\n","count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n","mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n","std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n","min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n","25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n","50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n","75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n","max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n","\n","          restecg     thalach       exang     oldpeak       slope          ca  \\\n","count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n","mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n","std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n","min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n","25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n","50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n","75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n","max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n","\n","             thal      target  \n","count  303.000000  303.000000  \n","mean     2.313531    0.544554  \n","std      0.612277    0.498835  \n","min      0.000000    0.000000  \n","25%      2.000000    0.000000  \n","50%      2.000000    1.000000  \n","75%      3.000000    1.000000  \n","max      3.000000    1.000000  "],"text/html":["\n","  <div id=\"df-8077a748-f74f-4f21-94b6-9fc7e8a6e707\">\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","        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<td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>54.366337</td>\n","      <td>0.683168</td>\n","      <td>0.966997</td>\n","      <td>131.623762</td>\n","      <td>246.264026</td>\n","      <td>0.148515</td>\n","      <td>0.528053</td>\n","      <td>149.646865</td>\n","      <td>0.326733</td>\n","      <td>1.039604</td>\n","      <td>1.399340</td>\n","      <td>0.729373</td>\n","      <td>2.313531</td>\n","      <td>0.544554</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>9.082101</td>\n","      <td>0.466011</td>\n","      <td>1.032052</td>\n","      <td>17.538143</td>\n","      <td>51.830751</td>\n","      <td>0.356198</td>\n","      <td>0.525860</td>\n","      <td>22.905161</td>\n","      <td>0.469794</td>\n","      <td>1.161075</td>\n","      <td>0.616226</td>\n","      <td>1.022606</td>\n","      <td>0.612277</td>\n","      <td>0.498835</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>29.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>94.000000</td>\n","      <td>126.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>71.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>47.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>120.000000</td>\n","      <td>211.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>133.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>55.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>130.000000</td>\n","      <td>240.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>153.000000</td>\n","      <td>0.000000</td>\n","      <td>0.800000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>61.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>140.000000</td>\n","      <td>274.500000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>166.000000</td>\n","      <td>1.000000</td>\n","      <td>1.600000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>77.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>200.000000</td>\n","      <td>564.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>202.000000</td>\n","      <td>1.000000</td>\n","      <td>6.200000</td>\n","      <td>2.000000</td>\n","      <td>4.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8077a748-f74f-4f21-94b6-9fc7e8a6e707')\"\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 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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":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4InaOSIUsfWP","executionInfo":{"status":"ok","timestamp":1653200234318,"user_tz":-330,"elapsed":24,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"fb25a289-d5e9-438f-d21d-f09474cacbc6"},"source":["# checking the distribution of Target Variable\n","heart_data['target'].value_counts()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    165\n","0    138\n","Name: target, dtype: int64"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"aSOBu4qDtJy5"},"source":["1 --> Defective Heart\n","\n","0 --> Healthy Heart"]},{"cell_type":"markdown","metadata":{"id":"tW8i4igjtPRC"},"source":["Splitting the Features and Target"]},{"cell_type":"code","metadata":{"id":"Q6yfbswrs7m3","executionInfo":{"status":"ok","timestamp":1653200234318,"user_tz":-330,"elapsed":22,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X = heart_data.drop(columns='target', axis=1)\n","Y = heart_data['target']"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XJoCp4ZKtpZy","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"14301a08-f0ce-41ef-ea17-b6a315aebef5"},"source":["print(X)"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","0     63    1   3       145   233    1        0      150      0      2.3   \n","1     37    1   2       130   250    0        1      187      0      3.5   \n","2     41    0   1       130   204    0        0      172      0      1.4   \n","3     56    1   1       120   236    0        1      178      0      0.8   \n","4     57    0   0       120   354    0        1      163      1      0.6   \n","..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n","298   57    0   0       140   241    0        1      123      1      0.2   \n","299   45    1   3       110   264    0        1      132      0      1.2   \n","300   68    1   0       144   193    1        1      141      0      3.4   \n","301   57    1   0       130   131    0        1      115      1      1.2   \n","302   57    0   1       130   236    0        0      174      0      0.0   \n","\n","     slope  ca  thal  \n","0        0   0     1  \n","1        0   0     2  \n","2        2   0     2  \n","3        2   0     2  \n","4        2   0     2  \n","..     ...  ..   ...  \n","298      1   0     3  \n","299      1   0     3  \n","300      1   2     3  \n","301      1   1     3  \n","302      1   1     2  \n","\n","[303 rows x 13 columns]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nukuj-YItq1w","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"ee5e6451-0b29-4a02-aebf-24367d73874b"},"source":["print(Y)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["0      1\n","1      1\n","2      1\n","3      1\n","4      1\n","      ..\n","298    0\n","299    0\n","300    0\n","301    0\n","302    0\n","Name: target, Length: 303, dtype: int64\n"]}]},{"cell_type":"markdown","metadata":{"id":"_EcjSE3Et18n"},"source":["Splitting the Data into Training data & Test Data"]},{"cell_type":"code","metadata":{"id":"a-UUfRUxtuga","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x7PrjC6zuf6X","executionInfo":{"status":"ok","timestamp":1653200234320,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"d78b7e79-4659-4101-c9d6-61df1d0d5981"},"source":["print(X.shape, X_train.shape, X_test.shape)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["(303, 13) (242, 13) (61, 13)\n"]}]},{"cell_type":"markdown","metadata":{"id":"beSkZmpVuvn9"},"source":["Model Training"]},{"cell_type":"markdown","metadata":{"id":"gi2NOWZjuxzw"},"source":["Logistic Regression"]},{"cell_type":"code","metadata":{"id":"4-Md74FYuqNL","executionInfo":{"status":"ok","timestamp":1653200234320,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["model = LogisticRegression()"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kCdHYxGUu7XD","executionInfo":{"status":"ok","timestamp":1653200234321,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"e24ea90f-2542-44ca-abbd-9d0c9cce231d"},"source":["# training the LogisticRegression model with Training data\n","model.fit(X_train, Y_train)"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"]},{"output_type":"execute_result","data":{"text/plain":["LogisticRegression()"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"ZYIw8Gi9vXfU"},"source":["Model Evaluation"]},{"cell_type":"markdown","metadata":{"id":"wmxAekfZvZa9"},"source":["Accuracy Score"]},{"cell_type":"code","metadata":{"id":"g19JaUTMvPKy","executionInfo":{"status":"ok","timestamp":1653200234321,"user_tz":-330,"elapsed":18,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy on training data\n","X_train_prediction = model.predict(X_train)\n","training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uQBZvBh8v7R_","executionInfo":{"status":"ok","timestamp":1653200234908,"user_tz":-330,"elapsed":605,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"69f64655-7f79-4bdf-a0f1-6288620f032b"},"source":["print('Accuracy on Training data : ', training_data_accuracy)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on Training data :  0.8512396694214877\n"]}]},{"cell_type":"code","metadata":{"id":"mDONDJdlwBIO","executionInfo":{"status":"ok","timestamp":1653200234908,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy on test data\n","X_test_prediction = model.predict(X_test)\n","test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_MBS-OqdwYpf","executionInfo":{"status":"ok","timestamp":1653200234909,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"bbe13e78-0dab-4925-e541-fb39be7d847f"},"source":["print('Accuracy on Test data : ', test_data_accuracy)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on Test data :  0.819672131147541\n"]}]},{"cell_type":"markdown","metadata":{"id":"jIruVh3Qwq0e"},"source":["Building a Predictive System"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9ercruC9wb4C","executionInfo":{"status":"ok","timestamp":1653200234909,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"e4cddd3b-acdb-482c-a25f-b720f02111c9"},"source":["input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)\n","\n","# change the input data to a numpy array\n","input_data_as_numpy_array= np.asarray(input_data)\n","\n","# reshape the numpy array as we are predicting for only on instance\n","input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n","\n","prediction = model.predict(input_data_reshaped)\n","print(prediction)\n","\n","if (prediction[0]== 0):\n","  print('The Person does not have a Heart Disease')\n","else:\n","  print('The Person has Heart Disease')"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["[0]\n","The Person does not have a Heart Disease\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n","  \"X does not have valid feature names, but\"\n"]}]},{"cell_type":"markdown","metadata":{"id":"FCHCMHpshHU4"},"source":["Saving the trained model"]},{"cell_type":"code","metadata":{"id":"cdmTOR4MhHCB","executionInfo":{"status":"ok","timestamp":1653200234910,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import pickle"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"4gN09lokhKuZ","executionInfo":{"status":"ok","timestamp":1653200234910,"user_tz":-330,"elapsed":10,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["filename = 'heart_disease_model.sav'\n","pickle.dump(model, open(filename, 'wb'))"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"IKW4D5CqhP5X","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the saved model\n","loaded_model = pickle.load(open('heart_disease_model.sav', 'rb'))"],"execution_count":24,"outputs":[]},{"cell_type":"code","source":["for column in X.columns:\n","  print(column)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1SiD-oDpPSxY","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"13c5618c-e337-4637-a35a-452e607e5ef4"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["age\n","sex\n","cp\n","trestbps\n","chol\n","fbs\n","restecg\n","thalach\n","exang\n","oldpeak\n","slope\n","ca\n","thal\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"A23YZI2tPaFk","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":8,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"execution_count":25,"outputs":[]}]}