Blood Donor Prediction.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
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   "id": "c61812b7",
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   "source": [
    "df = pd.read_csv(\"transfusion.csv\")"
   ]
  },
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   "id": "3eff74c7",
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   "source": [
    "df.head()"
   ]
  },
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   "id": "e2079729",
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   "outputs": [],
   "source": [
    "df.describe()"
   ]
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "627a4176",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1c3c68d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"whether he/she donated blood in March 2007\": \"donated\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f87d25a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"donated\", axis=1)\n",
    "y = df[\"donated\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c027339a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c1072eb9",
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   "outputs": [
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       "RandomForestClassifier(n_estimators=400)"
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     "execution_count": 34,
     "metadata": {},
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   "source": [
    "rf_model = RandomForestClassifier(n_estimators=400)\n",
    "rf_model.fit(X_train, y_train)"
   ]
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   "execution_count": 35,
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    "y_pred = rf_model.predict(X_test)"
   ]
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    "accuracy_score(y_test, y_pred)"
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   "id": "99b51872",
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    "pickle.dump(rf_model, open(\"rf.pkl\", \"wb\"))"
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