data_preprocessing.ipynb

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   "source": [
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import Imputer, LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "dataset = pd.read_csv('Data.csv')\n",
    "x = dataset.iloc[:,:-1].values\n",
    "y = dataset.iloc[:,3].values\n",
    "print(x)\n",
    "\n",
    "# handle missing data\n",
    "imputer = Imputer(missing_values=\"NaN\",strategy=\"mean\",axis=0)\n",
    "z = imputer.fit(x[:,1:3])\n",
    "x[:,1:3] = z.transform(x[:,1:3])\n",
    "print(x)\n",
    "\n",
    "# encode text data to, numbers\n",
    "labelencoder_x = LabelEncoder()\n",
    "x[:,0]=labelencoder_x.fit_transform(x[:,0])\n",
    "print(x)\n",
    "\n",
    "# encode the above numbers to ,only 1's and 0's\n",
    "onehotencoder = OneHotEncoder(categorical_features=[0])\n",
    "x = onehotencoder.fit_transform(x).toarray()\n",
    "print(x)\n",
    "\n",
    "# encode text data to, numbers\n",
    "labelencoder_y = LabelEncoder()\n",
    "y=labelencoder_y.fit_transform(y)\n",
    "print(y)\n",
    "\n",
    "# train data and test data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=0)\n",
    "print(x_train)\n",
    "print(x_test)\n",
    "print(y_train)\n",
    "print(y_test)\n",
    "\n",
    "# feature scaling, machine learning models rely on euclidian\n",
    "# distance, so transform the whole data\n",
    "sc_x = StandardScaler()\n",
    "x_train = sc_x.fit_transform(x_train)\n",
    "x_test = sc_x.transform(x_test)\n",
    "\n",
    "print(x_train)\n",
    "print(x_test)"
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