2. Data Preprocessing - Dealing with missing values in dataset.ipynb

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   "source": [
    "### In this we will see how to take care of missing values which are represented by 'nan' in our dataset. We will be calculating the mean in order to fill the missing values."
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   "source": [
    "#import the lib\n",
    "from sklearn.preprocessing import Imputer\n"
   ]
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   "metadata": {
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   "source": [
    "#creating the object of the class Imputer\n",
    "\n",
    "imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)\n",
    "imputer = imputer.fit(X[:,0:4])\n",
    "X[:, 0:4] = imputer.transform(X[:, 0:4])\n"
   ]
  }
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