Pycaret_Exercise.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,f1_score,roc_auc_score,recall_score,precision_score,matthews_corrcoef,cohen_kappa_score\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b2499cf",
   "metadata": {},
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   "source": [
    "data = pd.read_csv(r\"C:\\Users\\HP\\Desktop\\Oreilly\\Data/customer_churn.csv\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "ada4fc32",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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