heart-disease-prediction-using-logisticregression.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/yahyasoker/heart-disease-prediction-using-logisticregression?scriptVersionId=127915657\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"f9bce8b3","metadata":{"papermill":{"duration":0.005782,"end_time":"2023-05-01T14:24:28.700858","exception":false,"start_time":"2023-05-01T14:24:28.695076","status":"completed"},"tags":[]},"source":["### [1.Loading and Preparing at data](#1)\n","### [2.Training Data](#2)\n","    1-Train Test Split\n","    2-Logistic Regression\n","### [3.Model Evaluation](#3)\n","    1-Accuracy scores\n","    2-Building a Predictive System"]},{"cell_type":"markdown","id":"bd2bdb87","metadata":{"papermill":{"duration":0.004214,"end_time":"2023-05-01T14:24:28.709541","exception":false,"start_time":"2023-05-01T14:24:28.705327","status":"completed"},"tags":[]},"source":["<a id='1'></a>\n","#### Loading and Preparing at data"]},{"cell_type":"code","execution_count":1,"id":"b964bf04","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-05-01T14:24:28.720854Z","iopub.status.busy":"2023-05-01T14:24:28.720006Z","iopub.status.idle":"2023-05-01T14:24:29.779288Z","shell.execute_reply":"2023-05-01T14:24:29.77828Z"},"papermill":{"duration":1.068154,"end_time":"2023-05-01T14:24:29.78209","exception":false,"start_time":"2023-05-01T14:24:28.713936","status":"completed"},"tags":[]},"outputs":[],"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"]},{"cell_type":"code","execution_count":2,"id":"6aa586fe","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:29.792887Z","iopub.status.busy":"2023-05-01T14:24:29.792291Z","iopub.status.idle":"2023-05-01T14:24:29.818553Z","shell.execute_reply":"2023-05-01T14:24:29.817687Z"},"papermill":{"duration":0.034215,"end_time":"2023-05-01T14:24:29.82087","exception":false,"start_time":"2023-05-01T14:24:29.786655","status":"completed"},"tags":[]},"outputs":[],"source":["df = pd.read_csv(\"/kaggle/input/heart-disease/heart.csv\")"]},{"cell_type":"code","execution_count":3,"id":"3c67efb8","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:29.831027Z","iopub.status.busy":"2023-05-01T14:24:29.830708Z","iopub.status.idle":"2023-05-01T14:24:29.842927Z","shell.execute_reply":"2023-05-01T14:24:29.841862Z"},"papermill":{"duration":0.020284,"end_time":"2023-05-01T14:24:29.845474","exception":false,"start_time":"2023-05-01T14:24:29.82519","status":"completed"},"tags":[]},"outputs":[{"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"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":4,"id":"1c93c676","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:29.855844Z","iopub.status.busy":"2023-05-01T14:24:29.855278Z","iopub.status.idle":"2023-05-01T14:24:29.919204Z","shell.execute_reply":"2023-05-01T14:24:29.91793Z"},"papermill":{"duration":0.071227,"end_time":"2023-05-01T14:24:29.921322","exception":false,"start_time":"2023-05-01T14:24:29.850095","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>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>count</th>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <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>"],"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  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"code","execution_count":5,"id":"7198ed40","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:29.932844Z","iopub.status.busy":"2023-05-01T14:24:29.931978Z","iopub.status.idle":"2023-05-01T14:24:29.945749Z","shell.execute_reply":"2023-05-01T14:24:29.944638Z"},"papermill":{"duration":0.022107,"end_time":"2023-05-01T14:24:29.948185","exception":false,"start_time":"2023-05-01T14:24:29.926078","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>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>"],"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  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":6,"id":"f52f63ac","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:29.95992Z","iopub.status.busy":"2023-05-01T14:24:29.959624Z","iopub.status.idle":"2023-05-01T14:24:29.966995Z","shell.execute_reply":"2023-05-01T14:24:29.966004Z"},"papermill":{"duration":0.015573,"end_time":"2023-05-01T14:24:29.969188","exception":false,"start_time":"2023-05-01T14:24:29.953615","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["1    165\n","0    138\n","Name: target, dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df['target'].value_counts() # 1 Defective heart 0 Healthy heart"]},{"cell_type":"code","execution_count":7,"id":"c0e71281","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:29.98061Z","iopub.status.busy":"2023-05-01T14:24:29.980239Z","iopub.status.idle":"2023-05-01T14:24:29.98544Z","shell.execute_reply":"2023-05-01T14:24:29.984321Z"},"papermill":{"duration":0.013015,"end_time":"2023-05-01T14:24:29.987337","exception":false,"start_time":"2023-05-01T14:24:29.974322","status":"completed"},"tags":[]},"outputs":[],"source":["x = df.drop(columns='target', axis=1)\n","y = df['target']"]},{"cell_type":"markdown","id":"f5a39818","metadata":{"papermill":{"duration":0.004732,"end_time":"2023-05-01T14:24:29.997144","exception":false,"start_time":"2023-05-01T14:24:29.992412","status":"completed"},"tags":[]},"source":["<a id='2'></a>\n","### Training data"]},{"cell_type":"markdown","id":"3cbb7b05","metadata":{"papermill":{"duration":0.004464,"end_time":"2023-05-01T14:24:30.006431","exception":false,"start_time":"2023-05-01T14:24:30.001967","status":"completed"},"tags":[]},"source":["##### Train test split"]},{"cell_type":"code","execution_count":8,"id":"4139e7a6","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:30.018474Z","iopub.status.busy":"2023-05-01T14:24:30.018057Z","iopub.status.idle":"2023-05-01T14:24:30.024413Z","shell.execute_reply":"2023-05-01T14:24:30.023748Z"},"papermill":{"duration":0.013789,"end_time":"2023-05-01T14:24:30.026071","exception":false,"start_time":"2023-05-01T14:24:30.012282","status":"completed"},"tags":[]},"outputs":[],"source":["x_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.1,random_state=42)"]},{"cell_type":"markdown","id":"4e9494bb","metadata":{"papermill":{"duration":0.004365,"end_time":"2023-05-01T14:24:30.035458","exception":false,"start_time":"2023-05-01T14:24:30.031093","status":"completed"},"tags":[]},"source":["##### Logistic Regression"]},{"cell_type":"code","execution_count":9,"id":"ff806085","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:30.046218Z","iopub.status.busy":"2023-05-01T14:24:30.045648Z","iopub.status.idle":"2023-05-01T14:24:30.050282Z","shell.execute_reply":"2023-05-01T14:24:30.049276Z"},"papermill":{"duration":0.011915,"end_time":"2023-05-01T14:24:30.052153","exception":false,"start_time":"2023-05-01T14:24:30.040238","status":"completed"},"tags":[]},"outputs":[],"source":["model = LogisticRegression()"]},{"cell_type":"code","execution_count":10,"id":"69f5def6","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:30.062934Z","iopub.status.busy":"2023-05-01T14:24:30.062629Z","iopub.status.idle":"2023-05-01T14:24:30.106192Z","shell.execute_reply":"2023-05-01T14:24:30.10465Z"},"papermill":{"duration":0.052664,"end_time":"2023-05-01T14:24:30.109593","exception":false,"start_time":"2023-05-01T14:24:30.056929","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.7/site-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"]},{"data":{"text/plain":["LogisticRegression()"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["model.fit(x_train,y_train)"]},{"cell_type":"markdown","id":"0199f953","metadata":{"papermill":{"duration":0.005238,"end_time":"2023-05-01T14:24:30.12027","exception":false,"start_time":"2023-05-01T14:24:30.115032","status":"completed"},"tags":[]},"source":["<a id='3'></a>\n","#### Model Evaluation"]},{"cell_type":"markdown","id":"76f0f443","metadata":{"papermill":{"duration":0.0059,"end_time":"2023-05-01T14:24:30.131605","exception":false,"start_time":"2023-05-01T14:24:30.125705","status":"completed"},"tags":[]},"source":["##### Getting Accuracy scores"]},{"cell_type":"code","execution_count":11,"id":"5d5f85c7","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:30.143225Z","iopub.status.busy":"2023-05-01T14:24:30.142893Z","iopub.status.idle":"2023-05-01T14:24:30.152589Z","shell.execute_reply":"2023-05-01T14:24:30.151345Z"},"papermill":{"duration":0.018085,"end_time":"2023-05-01T14:24:30.154616","exception":false,"start_time":"2023-05-01T14:24:30.136531","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy on training data : 0.8566176470588235\n"]}],"source":["x_training_pred = model.predict(x_train)\n","training_data_acc = accuracy_score(x_training_pred,y_train)\n","print(f\"Accuracy on training data : {training_data_acc}\")"]},{"cell_type":"code","execution_count":12,"id":"d9eb5085","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:30.16613Z","iopub.status.busy":"2023-05-01T14:24:30.165825Z","iopub.status.idle":"2023-05-01T14:24:30.173201Z","shell.execute_reply":"2023-05-01T14:24:30.172069Z"},"papermill":{"duration":0.016388,"end_time":"2023-05-01T14:24:30.176226","exception":false,"start_time":"2023-05-01T14:24:30.159838","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy on test data : 0.8064516129032258\n"]}],"source":["\n","x_test_prediction = model.predict(x_test)\n","test_data_accuracy = accuracy_score(x_test_prediction, y_test)\n","print(f\"Accuracy on test data : {test_data_accuracy}\")"]},{"cell_type":"markdown","id":"93bec940","metadata":{"papermill":{"duration":0.004724,"end_time":"2023-05-01T14:24:30.186394","exception":false,"start_time":"2023-05-01T14:24:30.18167","status":"completed"},"tags":[]},"source":["##### Building a Predictive System"]},{"cell_type":"code","execution_count":13,"id":"6b93192b","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:24:30.198508Z","iopub.status.busy":"2023-05-01T14:24:30.197882Z","iopub.status.idle":"2023-05-01T14:24:30.206675Z","shell.execute_reply":"2023-05-01T14:24:30.205595Z"},"papermill":{"duration":0.017341,"end_time":"2023-05-01T14:24:30.209166","exception":false,"start_time":"2023-05-01T14:24:30.191825","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["[1]\n","The Person has Heart Disease\n"]},{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.7/site-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"]}],"source":["input_data = (70,5,2,111,200,0,0,120,0,1.2,10,2,2)\n","input_data_as_numpy_array= np.asarray(input_data)\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')"]}],"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":"3.7.12"},"papermill":{"default_parameters":{},"duration":11.094869,"end_time":"2023-05-01T14:24:30.837142","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-05-01T14:24:19.742273","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}