Day 50.ipynb

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   "source": [
    "# Recommendation System for Movies in Pytorch using Stack Auto Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
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   "source": [
    "# importing the Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the dataset\n",
    "movies = pd.read_csv('dataset\\ml-1m/movies.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
    "users = pd.read_csv('dataset\\ml-1m/users.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
    "ratings = pd.read_csv('dataset\\ml-1m/ratings.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the training set and the test set\n",
    "training_set = pd.read_csv('dataset\\ml-100k/u1.base', delimiter = '\\t')\n",
    "training_set = np.array(training_set, dtype = 'int')\n",
    "test_set = pd.read_csv('dataset\\ml-100k/u1.test', delimiter = '\\t')\n",
    "test_set = np.array(test_set, dtype = 'int')"
   ]
  }
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