07. BAYESIAN NETWORK (USING API).ipynb

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   "source": [
    "import sys \n",
    "from urllib.request import urlopen\n",
    "import numpy as np \n",
    "import pandas as pd"
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   "source": [
    "heartDisease_df = pd.read_csv('Datasets/heartDisease.csv') "
   ]
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    "heartDisease_df.drop(['ca', 'slope', 'thal', 'oldpeak'], axis=1, inplace=True)  "
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    "heartDisease_df.replace('?', np.nan, inplace=True)"
   ]
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   "source": [
    "from pgmpy.estimators import MaximumLikelihoodEstimator \n",
    "from pgmpy.models import BayesianModel\n",
    "model = BayesianModel([('age', 'trestbps'), ('age', 'fbs'), ('sex', 'trestbps'), ('exang', 'trestbps'), ('trestbps', 'heartdisease'),                           ('fbs', 'heartdisease'), ('heartdisease', 'restecg'), ('heartdisease', 'thalach'), ('heartdisease', 'chol')])\n",
    "\n",
    "model.fit(heartDisease_df, estimator=MaximumLikelihoodEstimator) "
   ]
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   "cell_type": "code",
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    "print(model.get_cpds('age')) \n",
    "print(model.get_cpds('chol')) \n",
    "print(model.get_cpds('sex'))\n",
    "model.get_independencies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pgmpy.inference import VariableElimination\n",
    "HeartDisease_infer = VariableElimination(model)"
   ]
  },
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   "cell_type": "code",
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    "q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'age': 28}) \n",
    "print(q['heartdisease'])"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'chol': 100}) \n",
    "print(q['heartdisease'])"
   ]
  }
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