Model 2 (Random Forest Regressor).ipynb

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      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
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      "0.9152358183003695\n",
      "Mean Absolute Error: 0.08476418169963051\n",
      "Mean Squared Error: 0.08476418169963051\n",
      "Root Mean Squared Error: 0.29114288880141054\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import pickle\n",
    "\n",
    "data = pd.read_csv(\"spambase.csv\", sep=\",\")\n",
    "target = data.pop('class')\n",
    "sc = StandardScaler()\n",
    "train = sc.fit_transform(data)\n",
    "regressor = RandomForestRegressor(random_state=888)\n",
    "regressor.fit(train, target)\n",
    "y_pred = regressor.predict(train)\n",
    "target = np.array(target)\n",
    "y_pred = np.array(y_pred).astype('int64')\n",
    "acc = accuracy_score(target, y_pred)\n",
    "print(acc)\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target, y_pred)))"
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