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    "## This is the Q2 Notebook!\n",
    "\n",
    "It's tracked via GitHub! hence the need for this line for the init commit"
   ],
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    "## Part A\n",
    "\n",
    "Q: A. Using (Rating + Reviews + Content Rating + Size in Bytes +\n",
    "Installs_Num), using Logistic regression and KNN, find and discuss the best\n",
    "classification model to predict “Category” (use the training/validation/test\n",
    "partition without cross-validation). **[8 marks]**\n"
   ],
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   "source": [
    "## Part B\n",
    "\n",
    "Q: Using (Rating + Reviews + Category + Size in Bytes + Installs_Num),\n",
    "using Logistic regression and KNN, find and discuss the best classification model\n",
    "to predict “Content Rating” (use the training/validation/test partition without\n",
    "cross-validation). **[7 marks]**"
   ],
   "id": "f15aa6cca58826fc"
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  {
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   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
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   "source": [
    "## Part C\n",
    "\n",
    "Q: By considering Installs_Num as categorical feature and using (Rating +\n",
    "Reviews + Category + Size in Bytes + Content Rating), find and discuss the\n",
    "best classification model to predict “Installs_Num” (using Logistic regression and\n",
    "KNN) (use the training/test partition without cross-validation) **[8 marks]**"
   ],
   "id": "86c040f5e043a39c"
  },
  {
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   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
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   "id": "f3535461938f94f5"
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