Project Part 1 (Template).ipynb

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    "#### Instruction\n",
    "- Use this template to develop your project. Do not change the steps. \n",
    "- For each step, you may add additional cells if needed.\n",
    "- Describe the steps in the \"Description:\" field."
   ]
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    "#### Group Information\n",
    "\n",
    "Group No: Cancer_2\n",
    "\n",
    "- Member 1: Foo Ling Zhen\n",
    "- Member 2: Dean Lau Sheng Ting\n",
    "- Member 3: Looi Wei En\n",
    "- Member 4: Eason Peng \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "#### Import libraries"
   ]
  },
  {
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   "execution_count": 2,
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   "outputs": [],
   "source": [
    "%config Completer.use_jedi=False"
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    "#### Load the dataset"
   ]
  },
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    "#### Split the dataset\n",
    "Split the dataset into training, validation and test sets."
   ]
  },
  {
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   "execution_count": 9,
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    "#### Data preprocessing\n",
    "Perform data preprocessing such as normalization, standardization, label encoding etc.\n",
    "______________________________________________________________________________________\n",
    "Description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "#### Feature Selection\n",
    "Perform feature selection to select the relevant features.\n",
    "______________________________________________________________________________________\n",
    "Description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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    "#### Data modeling\n",
    "Build the machine learning models. You must build atleast two (2) predictive models. One of the predictive models must be either Decision Tree or Support Vector Machine.\n",
    "______________________________________________________________________________________\n",
    "Description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "#### Evaluate the models\n",
    "Perform a comparison between the predictive models. <br>\n",
    "Report the accuracy, recall, precision and F1-score measures as well as the confusion matrix if it is a classification problem. <br>\n",
    "Report the R2 score, mean squared error and mean absolute error if it is a regression problem.\n",
    "______________________________________________________________________________________\n",
    "Description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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