2022_Individual Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "interracial-navigator",
   "metadata": {},
   "source": [
    "# Spring 2022 Individual Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "extensive-reader",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "boolean-point",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "seventh-validity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MO HLADR+ MFI (cells/ul)', 'Neu CD64+MFI (cells/ul)', 'CD3+T (cells/ul)', 'CD8+T (cells/ul)', 'CD4+T (cells/ul)', 'NK (cells/ul)', 'CD19+ (cells/ul)', 'CD45+ (cells/ul)', 'Age', 'Sex 0M1F', 'Mono CD64+MFI (cells/ul)', 'label']\n"
     ]
    }
   ],
   "source": [
    "# Extract dataset\n",
    "data_df_original = pd.read_csv(\"train.csv\",index_col=0)\n",
    "data_df_test = pd.read_csv(\"test.csv\",index_col=0)\n",
    "data_df = data_df_original.copy()\n",
    "data_attribute = data_df.columns.values.tolist()\n",
    "print(data_attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "protective-briefing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MO HLADR+ MFI (cells/ul)    1\n",
      "Neu CD64+MFI (cells/ul)     1\n",
      "CD3+T (cells/ul)            0\n",
      "CD8+T (cells/ul)            0\n",
      "CD4+T (cells/ul)            0\n",
      "NK (cells/ul)               0\n",
      "CD19+ (cells/ul)            0\n",
      "CD45+ (cells/ul)            0\n",
      "Age                         0\n",
      "Sex 0M1F                    0\n",
      "Mono CD64+MFI (cells/ul)    1\n",
      "label                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check empty rows\n",
    "data_null_info = data_df.isnull().sum()\n",
    "print(data_null_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "graphic-image",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MO HLADR+ MFI (cells/ul)    0\n",
      "Neu CD64+MFI (cells/ul)     0\n",
      "CD3+T (cells/ul)            0\n",
      "CD8+T (cells/ul)            0\n",
      "CD4+T (cells/ul)            0\n",
      "NK (cells/ul)               0\n",
      "CD19+ (cells/ul)            0\n",
      "CD45+ (cells/ul)            0\n",
      "Age                         0\n",
      "Sex 0M1F                    0\n",
      "Mono CD64+MFI (cells/ul)    0\n",
      "label                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Use Median to fill in the empty entries\n",
    "median_col2 = data_df[\"MO HLADR+ MFI (cells/ul)\"].median()\n",
    "median_col3 = data_df[\"Neu CD64+MFI (cells/ul)\"].median()\n",
    "median_col12 = data_df[\"Mono CD64+MFI (cells/ul)\"].median()\n",
    "\n",
    "data_df[\"MO HLADR+ MFI (cells/ul)\"]= data_df[\"MO HLADR+ MFI (cells/ul)\"].fillna(median_col2)\n",
    "data_df[\"Neu CD64+MFI (cells/ul)\"]= data_df[\"Neu CD64+MFI (cells/ul)\"].fillna(median_col3)\n",
    "data_df[\"Mono CD64+MFI (cells/ul)\"]= data_df[\"Mono CD64+MFI (cells/ul)\"].fillna(median_col12)\n",
    "\n",
    "data_null_info = data_df.isnull().sum()\n",
    "print(data_null_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unauthorized-library",
   "metadata": {},
   "source": [
    "### Extract Training and Validation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "different-boxing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n",
      "27\n"
     ]
    }
   ],
   "source": [
    "# Extract Training and Validation Dataset\n",
    "X = data_df.drop([\"label\"], axis=1)\n",
    "y = data_df[\"label\"]\n",
    "# Extract Testing Dataset\n",
    "X_test = data_df_test.copy()\n",
    "\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size=0.3,random_state=100)\n",
    "print(len(X_train))\n",
    "print(len(X_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enhanced-gates",
   "metadata": {},
   "source": [
    "### Training and Validation Stage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "current-norfolk",
   "metadata": {},
   "source": [
    "##### Random Forest Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "removed-history",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Estimators: 50\n",
      "validation accuracy: 0.8888888888888888\n"
     ]
    },
    {
     "data": {
      "image/png": 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sri2K+yHpOzYeeLEo5xN1O64PkXbAL5C65XYu4utVr4fymJk1U9TKn44sjm/64gGk1NMHzWxgUzoW+eOkw7XmK77whJm1Rbm+S2r+/iiyeLzseDrNzWozswZcczQza2C+7nPUggsECw8tOwzrg03WWqvsEKwPnnhiIlOnPq/mr+yZhg8L3prZ2otffuOaiGh48ZNOm6+TIwsPhS1WKzsK64Nx1/6l7BCsD0aNausqe7ObPhO2bPF3eu2EZmeddcz8nRzNbGDQXFU+5wknRzMrX/Vyo5OjmVWAa45mZt0I6HJyNDObU/Vyo5OjmZVNblabmTVUvdzo5GhmJROVTI4+fdDMyie19mhajMZIek7S/d2mHyrpIUkPSPp/Pb2/nmuOZla+IR2rOp5DukBy7V5ESNqedNOzDSLiTent+133HlKnIjIza4v68GgiIm4mXem93kHADyLizeI1z7USlpOjmZWv9Wb1cEnj6h4HtlD6WsA2km6X9DdJm7YSkpvVZla+1qtpUyOilbti1luAdH+fLUi3cL1Y0urR5GK2rjmaWfk61KzuwSTg8kjuIN21tOnVfZwczaxcIg3ItPJoz+8p7tooaS3SLW6b3o3UzWozK1nnzpCRNJZ0f/PhkiaR7js+BhhTHN7zFrBfsyY1ODmaWRV06EieiNi7h1mf7WtZTo5mVj6fW21m1k1FTx90cjSz8rnmaGbWQFfZAczJydHMyiVcczQza6h6udHJ0cwqwDVHM7MGqpcbnRzNrGy+h4yZ2ZwEavEqD03P+esgJ0czK51arDk6OZrZoFLBVrWTo5mVK12xrLXsOHPehjIbJ0czK1c1x2OcHM2sfK3WHPuTk6OZla6CudHJ0czKVdFTq30PGTMrn6SWHi2UM0bSc8UtEbrPO0JSSGp6cy1wcjSzsrV4y+oWa5fnADvPsQhpJPBB4KlWw3JyNLOStVZrbKXmGBE3A883mPUz4Cj6cBy5+xzNrFQCulrvcxwuaVzd8zMj4sxey5f2ACZHxD2tnokDTo5mVgF9SFpTI2JUH8pdFDiO1KTuEzerzax0Hexz7G4NYDXgHklPACsBd0t6V7M3uuZoZuWah2fIRMR9wPJvLyolyFERMbXZe11zNLNSpeMcO3Yoz1jgNmBtSZMkHdBuXK45mlnpOlVzjIi9m8xftdWynBzNrHRDhlTvFBknRzMrXfVSo5OjmZUsjURXLz06OZpZ6SqYG50czax8FexydHI0s3IJeUDG+u7sw49kty0257kXX2T9A78EwIXHHc/aI1cCYKnFhvHiq6+w0UFfKTNM68HVd97B1351GjNnzeKLO+/CMXv1eqTJ4OQ+x7knaWfgZKALOCsiflBySPPcOdddwylX/p7fHnX029P2OunEt//+8YFf5qVXXy0jNGti5syZHHzKL7nuBz9kpeHLsemhB7P7lluxziqrlB1a5VQwNw6cM2QkdQGnArsA6wB7S1qn3KjmvVvuu4/nX365x/mf3m47xv71r/0YkbXqjocfZs0VV2T1d6/IgkOHstd2o/nDP/5edliVpBYf/WnAJEdgM+CxiPh3RLwFXAjsUXJMpdpm/fX5zwsv8NjTk8sOxRqYPHUqI5d7+7ReVlpuOSZPm1ZiRNXVqdMHO2kgJccRwMS655OKabORdKCkcZLGMX1GvwVXhr1H7+Baow14tXvIzKOr8rRtQPU5tqK48OWZAFpikZav+jvQdA0Zwsff/342OfigskOxHowYPpyJU557+/mkKVMYseyyJUZUUYIhXdWrp1Uvop5NBkbWPV+pmDYo7bTxJjw08SkmT2165SUryaZrr82jkyfz+DPP8Nb06Vz4t5vYfcutyg6rgjp3m4ROGkg1xzuB90hajZQU9wL2KTekee93xx7H6PdtwPAll2TiBWPJzjuXMVdfzV6jR7tJXXELdHVxyiGH8qHjjmHmrFl84UM7s+6qq5YdViVVcbR6wCTHiJgh6RDgGtKhPGMi4oGSw5rn9vn+SQ2nf/7HP+rnSKwdu262ObtutnnZYVRa7XqOVTNgkiNARFwFXFV2HGbWQQJV8AyZgdTnaGbzqQ5eCXyMpOck3V837UeSHpJ0r6QrJC3VSkxOjmZWMjFkyJCWHi04B9i527TrgPUi4n3AI8CxrRTk5GhmpZJAQ1p7NBMRNwPPd5t2bUTUDnr+P9KRLk0NqD5HM5s/9WFAZrikcXXPzyyObW7VF4CLWnmhk6OZla4PyXFqRIxqcxn/C8wALmjl9U6OZla+eXwoj6T9gd2AHSOipTPnnBzNrFyCIV3zLjkWlzo8CtguIl5r9X0ekDGzUqmDpw9KGgvcBqwtaZKkA4BTgMWB6ySNl3R6K3G55mhmpevUGTIR0ehS62e3U5aTo5mVy7dJMDNrrIqnDzo5mlnJ+v9yZK1wcjSzUkn41qxmZo245mhm1oD7HM3MuvNotZnZnGp3H6waJ0czK5lHq83M5lTRW7M6OZpZ+VxzNDObUwVzo5OjmZVLwJAKZkcnRzMrmQdkzMzmIMECrd1ZsF85OZpZ6dysNjNrYFA3qyX9EujxxjYRcVh/xWJm1SHEEDqTHCWNId1I67mIWK+YtgzpdqyrAk8An46IF5qV1Z8N/XHAXb08zGyQGqLWHi04B9i527RjgBsi4j3ADcXzpvqt5hgR59Y/l7RoX+4EZmbzqQ5eeCIibpa0arfJewCji7/PBW4Cjm5WVr8PEUnaUtKDwEPF8w0kndbfcZhZNYg0Wt3KAxguaVzd48AWFrFCRDxT/P0ssEIrcZUxIPNz4EPAlQARcY+kbUuIw8wqoI8HgU+NiFHtLisiQlKPYx/1Sjm4KCImdps0s4w4zKwK1PK/Nv1H0rsBiv+fa+VNZSTHiZK2AkLSUElHAhNKiMPMKmKI1NKjTVcC+xV/7wf8oaWY2l3aXPgKcDAwAnga2LB4bmaDkNS55ChpLHAbsLakSZIOAH4AfEDSo8BOxfOm+r3PMSKmAp/p7+WaWXV1degeMhGxdw+zduxrWWWMVq8u6Y+Spkh6TtIfJK3e33GYWTXUBmTmYbO6LWU0q38HXAy8G1gRuAQYW0IcZlYJrSXGwZAcF42I8yJiRvE4H1i4hDjMrCLm8Wh1W/rz3Oplij//IukY4ELSudZ7Alf1VxxmVi21AZmq6c8BmbtIybC2Fb5cNy+AY/sxFjOrkEGdHCNitf5alpkNHEJ0+WK3iaT1gHWo62uMiN+WEYuZla9DR/J0VL8nR0kZ6QoZ65D6GncBbgWcHM0GIUHHrufYSWXUZT9JOiDz2Yj4PLABsGQJcZhZFXTwDJlOKqNZ/XpEzJI0Q9ISpJPAR5YQh5lVxKC+TUKdcZKWAn5NGsF+hXQupJkNQr5vdSEivlr8ebqkq4ElIuLe/o7DzKpikI9WS9q4t3kRcXd/xWJm1eGDwOEnvcwLYIdOL3CD94zguitP6nSxNg+NmeCDFgaSqW9M60g51as39u9B4Nv317LMbGDxgIyZWTdVHZCpYm3WzAYV0aXWHi2VJn1D0gOS7pc0VlJbV/1ycjSzUnXyYreSRgCHAaMiYj2gC9irnbjKuBK4JH1W0gnF85UlbdbfcZhZRSidW93Ko0ULAItIWgBYlHSvqj4ro+Z4GrAlULvXw8vAqSXEYWYV0YeL3Q6XNK7ucWB9ORExGfgx8BTwDPBSRFzbTkxlDMhsHhEbS/onQES8IGnBEuIwswro44DM1IgY1WNZ0tLAHsBqwIvAJZI+W9xxoE/KqDlOl9RFOrYRScsBs0qIw8wqooPN6p2AxyNiSkRMBy4HtmonpjJqjr8ArgCWl/Q90lV6ji8hDjOrACG61LF62lPAFpIWBV4nXQFsXDsFlXFu9QWS7iIFLeCjETGhv+Mws+ro1MVuI+J2SZcCdwMzgH8CZ7ZTVhkXu10ZeA34Y/20iHiqv2MxswpQZ8+QiYgMyOa2nDKa1X/mnRttLUzqOH0YWLeEWMysZFW9EngZzer1658XV+v5ag8vN7NBwPeQaSAi7pa0edlxmFl5fOEJQNLhdU+HABvT5hHsZjbwCVjAyRGAxev+nkHqg7yshDjMrBLkmmNx8PfiEXFkfy7XzKpr0F8JXNICETFD0tb9tUwzGxgG+4DMHaT+xfGSrgQuAV6tzYyIy/sxFjOrEPlQHiAd2ziNdM+Y2vGOQToH0swGGQELVLDq2J/JcflipPp+3kmKNdGPcZhZxQz2mmMXMAwabgUnR7NBKl2yrOwo5tSfyfGZiPhOPy7PzAaCFm+B0N/6MzlWb+3NrBIG+3GOO/bjssxsgEgXnqiefkuOEfF8fy3LzAaWriHVS4+lX3jCzAa3QV9zNDPrSRX7HKuYsM1sMClGq1t5tFaclpJ0qaSHJE2QtGU7YbnmaGalEh0/lOVk4OqI+GRx2+dF2ynEydHMStepZrWkJYFtgf0BIuIt4K12ynKz2sxK1yW19ACGSxpX9ziwW1GrAVOA30j6p6SzJC3WTkyuOZpZqdLpgy3XHKdGxKhe5i9AuvrXocVtWk8GjgG+1de4XHM0s9KpxX8tmARMiojbi+eXkpJlnzk5mlnppNYezUTEs8BESWsXk3YEHmwnJjerzaxUQp2+b/WhwAXFSPW/gc+3U4iTo5mVSzCkg9csi4jxQG/9ki1xcjSz0nW45tgRTo5mVipRzdMHnRzNrHRVHBl2cjSz0rnmaGY2Bzk5mpl1J1E7NbBSnBzNrHSD/dasZmZz8K1Zzcx64JqjmVkDg/2+1WZmcxCt3wKhPzk5mlnpnBxtrrzx1nT2OPbXvDl9BjNnzmK3rdfj6H12Kjss68Wz/3mJ08+9+e3nU6a+wkd33YAPjF6nxKiqp3qpcYAlR0ljgN2A5yJivbLj6W8LDV2Ay048gGGLLMT0GTP5yDFnsOPGazHqvSuXHZr14F0rLMm3j/oIALNmzeKIEy5lo/f586onVbPmWMVTGntzDrBz2UGURRLDFlkIgOkzZzJ9xqxKnllgjT34yLMsP3xxhi8zrOxQKkdSS4/+NKBqjhFxs6RVy46jTDNnzmKnw0/l8Wem8YVdt2CTtUeWHZK16I67H2ezjVcrO4xKquIufqDVHJuSdGDtzmTTpvy37HA6rqtrCH89+VDuGXM0/3x0IhOefLbskKwFM2bM5J77JzFqw1XKDqVyhOjSkJYeLZUndRV3HvzT3MQ13yXHiDgzIkZFxKhll1ui7HDmmSWHLcLW66/OjXc/WnYo1oL7Jkxm5ZWWYcklFik7lErq1D1kCl8DJsxtTPNdcpyfTX3pFV565XUAXn9zOn8b/xjvWWm5kqOyVtx+1xNs7iZ1jzp190FJKwEfBs6a25gGVJ/jYPef51/m0J9fysxZQcQsdn//+nxw0/eWHZY18eab03nw4af53J5blB1KZXXw9MGfA0cBi89tQQMqOUoaC4wGhkuaBGQRcXa5UfWfdVd7NzeefGjZYVgfLbTQUH7x/b3KDqO6RF9GZIZLGlf3/MyIOBNAUu0wv7skjZ7bsAZUcoyIvcuOwcw6rw81x6kR0dOdBbcGdpe0K7AwsISk8yPis+3E5D5HMytV7dzqVh69iYhjI2KliFgV2Au4sd3ECAOs5mhm8ydfsszMrIFOn/0SETcBN81NGU6OZla66tUbnRzNrGRpsLp66dHJ0cxKV8Wr8jg5mlnJfN9qM7OG3Kw2M+vGfY5mZo307Yo7/cbJ0cxK55qjmVkDHq02M5tD3y7L01+cHM2sVKLzpw92gpOjmZWueqnRydHMKsADMmZmDbhZbWbWjYAhrjmamXXnc6vNzBqqYp+j7yFjZuUqTh9s5dG0KGmkpL9KelDSA5K+1m5YrjmaWak6fOGJGcAREXG3pMWBuyRdFxEP9rUgJ0czK5mAro6UFBHPAM8Uf78saQIwAnByNLOBpw81x+GSxtU9PzMizmxYprQqsBFwezsxOTmaWQW0nBynRsSopqVJw4DLgK9HxH/bicjJ0cwqoHNjw5KGkhLjBRFxebvlODmaWck6d7VbpQMmzwYmRMRP56YsH8pjZqVTi/9asDWwL7CDpPHFY9d2YnLN0cwqoGOj1bfSoYv8ODmaWcnS2dVV4+RoZqWr4umDTo5mVgFOjmZm3bhZbWbWA9cczczmoA6NVneSk6OZlaxzB4F3kpOjmVWA+xzNzGbT4es5doyTo5lVgJOjmVk3nbvYbSc5OZpZ6eQ+RzOz7oSb1WZmDTk5mpk14Ga1mdkc5IPAzcy6q+ZodfXqsmY2CKnFRwslSTtLeljSY5KOaTciJ0czK5kQQ1p6NC1J6gJOBXYB1gH2lrROO1E5OZpZBXSs5rgZ8FhE/Dsi3gIuBPZoK6KIaOd9A4KkKcCTZccxDwwHppYdhPXJ/PqZrRIRy81NAZKuJm2fViwMvFH3/MyIOLOurE8CO0fEF4vn+wKbR8QhfY1rvh6QmdsPraokjYuIUWXHYa3zZ9aziNi57BgacbPazOYnk4GRdc9XKqb1mZOjmc1P7gTeI2k1SQsCewFXtlPQfN2sno+d2fwlVjH+zPpBRMyQdAhwDengyTER8UA7Zc3XAzJmZu1ys9rMrAEnRzOzBpwcB5hOnRpl/UPSGEnPSbq/7Fisb5wcB5BOnhpl/eYcoJLH8VnvnBwHlo6dGmX9IyJuBp4vOw7rOyfHgWUEMLHu+aRimpl1mJOjmVkDTo4DS8dOjTKz3jk5DiwdOzXKzHrn5DiARMQMoHZq1ATg4nZPjbL+IWkscBuwtqRJkg4oOyZrjU8fNDNrwDVHM7MGnBzNzBpwcjQza8DJ0cysASdHM7MGnBwHMUkzJY2XdL+kSyQtOhdlnVPc+Q1JZ/V2QQxJoyVt1cYynpA0x13qepre7TWv9HFZ35Z0ZF9jtPmHk+Pg9npEbBgR6wFvAV+pnymprdtoRMQXI+LBXl4yGuhzcjTrT06OVnMLsGZRq7tF0pXAg5K6JP1I0p2S7pX0ZQAlpxTXlrweWL5WkKSbJI0q/t5Z0t2S7pF0g6RVSUn4G0WtdRtJy0m6rFjGnZK2Lt67rKRrJT0g6SxauKu7pN9Luqt4z4Hd5v2smH6DpOWKaWtIurp4zy2S3tuRrWkDnm+wZbUa4i7A1cWkjYH1IuLxIsG8FBGbSloI+Luka4GNgLVJ15VcAXgQGNOt3OWAXwPbFmUtExHPSzodeCUifly87nfAzyLiVkkrk84A+h8gA26NiO9I+jDQytklXyiWsQhwp6TLImIasBgwLiK+IemEouxDSDe++kpEPCppc+A0YIc2NqPNZ5wcB7dFJI0v/r4FOJvU3L0jIh4vpn8QeF+tPxFYEngPsC0wNiJmAk9LurFB+VsAN9fKioiermu4E7CO9HbFcAlJw4plfLx4758lvdDCOh0m6WPF3yOLWKcBs4CLiunnA5cXy9gKuKRu2Qu1sAwbBJwcB7fXI2LD+glFkni1fhJwaERc0+11u3YwjiHAFhHxRoNYWiZpNCnRbhkRr0m6CVi4h5dHsdwXu28DM3CfozV3DXCQpKEAktaStBhwM7Bn0Sf5bmD7Bu/9P2BbSasV712mmP4ysHjd664FDq09kbRh8efNwD7FtF2ApZvEuiTwQpEY30uqudYMAWq1331IzfX/Ao9L+lSxDEnaoMkybJBwcrRmziL1J95d3CTqDFKL4wrg0WLeb0lXnplNREwBDiQ1Ye/hnWbtH4GP1QZkgMOAUcWAz4O8M2qek5LrA6Tm9VNNYr0aWEDSBOAHpORc8yqwWbEOOwDfKaZ/BjigiO8BfNsJK/iqPGZmDbjmaGbWgJOjmVkDTo5mZg04OZqZNeDkaGbWgJOjmVkDTo5mZg38f52krLvRbjChAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######################################\n",
    "# Random Forest Classifiers\n",
    "#######################################\n",
    "tuned_parameters = [{'n_estimators': [10,20,30,40,50,60,70,80,90,100]}]\n",
    "\n",
    "# Determine best n_estimator \n",
    "clf_grid = GridSearchCV(RandomForestClassifier(random_state=20), tuned_parameters, cv=5,scoring='accuracy')\n",
    "clf_grid.fit(X_train.values, y_train.values)\n",
    "\n",
    "# Training RandomForest with best n_estimator\n",
    "clf_model_randomforest = RandomForestClassifier(max_depth=100, n_estimators=clf_grid.best_params_[\"n_estimators\"],\n",
    "                               criterion= \"gini\",random_state=20)\n",
    "clf_model_randomforest.fit(X_train.values, y_train.values)\n",
    "\n",
    "# Validate RandomForest with Validation dataset\n",
    "predict_y_validation = clf_model_randomforest.predict(X_validation.values)\n",
    "accuracy_score_results = accuracy_score(y_validation.values, predict_y_validation)\n",
    "print(\"Number of Estimators:\",clf_grid.best_params_[\"n_estimators\"] )\n",
    "print(\"validation accuracy:\", accuracy_score_results)\n",
    "\n",
    "#########################################\n",
    "### Display Confusion Matrix\n",
    "ConfusionMatrixDisplay.from_predictions(y_validation.values, predict_y_validation, cmap='YlGn')\n",
    "plt.title(\"\"\"Confusion Matrix for GridSearchCV: Accuracy{:.4}\"\"\".format(accuracy_score_results),color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loose-teaching",
   "metadata": {},
   "source": [
    "### Testing Stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "protecting-intake",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Generate Test Prediction \n",
    "Y_test_prediction_randomforest = clf_model_randomforest.predict(X_test.values).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "commercial-counter",
   "metadata": {},
   "source": [
    "### Generate Submission csv from the RandomForest Classifier Test Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tutorial-newfoundland",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_id_list = X_test.index.tolist()\n",
    "\n",
    "test_df = pd.DataFrame({\"id\":y_id_list, \"label\":Y_test_prediction_randomforest})\n",
    "test_df.to_csv(\"submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outdoor-wilderness",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}