multi_normalising.ipynb

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     "text": [
      "(26638, 9)\n",
      "(6661, 9)\n"
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   "source": [
    "# We want 80% of the data to be used for training, and 20% for testing\n",
    "n_train_rows = int(dataset.shape[0]*.8)-1\n",
    "\n",
    "# split into train and test sets but keep all 9 columns\n",
    "train = dataset.iloc[:n_train_rows, :]\n",
    "test = dataset.iloc[n_train_rows:, :]\n",
    "\n",
    "# The total rows of the two datasets should equal the total amount of rows in your CSV\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise features\n",
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "training_set_scaled = sc.fit_transform(train.values)\n",
    "test_set_scaled = sc.fit_transform(test.values)"
   ]
  }
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