Titanic Prediction DecisionTree and RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Sex         891 non-null int64\n",
      "Age         891 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Fare        891 non-null float64\n",
      "Embarked    891 non-null int64\n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 55.8 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "98.2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3KitvZv0XQ2r7qKuSY2xn9stXgX1Hnfs0cGJE7Era1q6R+2U/0oyXWcAC4Mz+VNPMBmVotdo+6qrkGNu5UXq1pJmjTwOb5cebs2Yu+lzg3DwT5hpJUyRN9+5HZvVRx95pp0qOsdsx9Q8Bl0s6ldTbf10+v7Y0AW7UzWqi5Ol+DSXH2O3io/cDx0TENsAxQGN7O6cJMKu5kvOiNJQcY7eN+nzgm/nxN1iTibGjNAERMTsiZh+06cwuq2Fm/TYUavuoq5Jj7LZR/y3wxvx4L+Du/Hgx8K48C2Z34BGPp5vVy9DqDdo+6qrkGLtNE/Ae4HRJk4EnSDNdAC4F9iflUX8cOKyCOptZhUqew91Qcoy9pAl4VYtrAziy10qZ2fgpeWZIQ8kxekWpmY1Qx3HkTpUcoxt1Mxuh5Ol+DSXH2E6agG0kXSnpDkm3STo6n99C0hJJd+efU/N5pwowq7GI9o+xSNpX0l25PTiuxesbS7ogv35t80JHScfn83dJ2scxtqedW7urgL+LiB1I29cdKWlH4DhgaUTMApbm5+BUAWa1tnpog7aPdZE0CfgiqU3YETg0tx3NDgdWRsR2wGnAKfm9OwLzgJeT0pR8Kf8+xziGMRv1iLg/Im7Kjx8F7iCtEp0LLMyXLQQOzI+HUwVExDXAFEnTe62omQ1GH3uxc4BlEXFPRDwFnE9qH5o1tyMXAnvn/Y7nAudHxJMRcS9pRt0c+qTkGDuahJm/NrwSuBbYujEHPf/cKl+2tlQBo3+XV5SaTUCdLMxp/hznY0HTr2qnLRi+JiJWAY8A09p8r2Nsoe0bpZKeA1wEfCgi/pD+0LS+tMW5Z/y988bTZhNTJzcRmz/HLbTTFqztmrZTjnSj5Bjb6qlL2pDUoJ8XEY30AA80hlXyzxX5fNupAsxs4unjEvp22oLha/Jixs2Bh9p8b9dKjrGd2S8iJey6IyI+2/TSYlIOGPLPbzWdd6oAs5qKDo4xXA/MkrStpI1INwUXj7qmuR05GLgiL2JcDMzLM0e2JU28uK6nwJqUHGM7wy9/DrwTuFXSzfncPwCfAhZJOhy4Dzgkv+ZUAWY1NtaMj3ZFxCpJRwGXA5OAcyLiNkknATdExGJSh/FrkpaReq/z8ntvk7QIuJ00A+/IiFjdl4pRdoyKCZAEwWPqZv03e/klXa2w+cHzD2778/j6311Yy1U8JcfoFaVmNkK0vH9XlpJjdKNuZiMMrQffm0uOsZc0Af8i6c6cCuBiSVOa3lPZ8l4zq9YQavuoq5Jj7CVNwBJgp4jYGfg5cDxUv7zXzKq1GrV91FXJMXadJiAivptXRwFcQ5pjCRUv7zWzagVq+6irkmPsJU1As3cD38mPnSbArMaGOjjqquQYu04T0HT+Y6QhmvMap1q83WkCzGqijg1Zp0qOsa1GfS1pApA0H3grsHesmfDuNAFmNVbHIYdOlRxj12kCJO0LfBQ4ICIeb3pLpct7zaxaQ2r/qKuSY+wlTcAZwMbAkpyx8ZqIeF/Vy3vNrFp1nPHRqZJjHLNRj4gf0nqc/NJ1vOdk4OQe6mVm46Tk8eaGkmP0ilIzG2Fo7XslFKPkGN2om9kI68NUtJJj7DpNQNPrH5YUkrbMzyXpjJwm4BZJu1VVeTPrv5LncDeUHGM7PfVGmoCbJD0XuFHSkoi4XdI2wJtJ+dQb9iPNeJkFvAY4M/80sxpYVfDQREPJMXadJiC/fBrwEUZ+m5kLnBvJNcCUxrZ3Zjbx9XFXoAmr5Bi7ThMg6QDgNxHx01GXOU2AWY2VPIe7oeQYu0oTQBqS+RjwllaXtjjnNAFmNVHHceROlRxjV2kCJL0C2Bb4aV54NAO4SdIcnCbArNbWhx5WyTGO2ai3ShMQEbcCWzVd80tgdkQ8KGkxcJSk80k3SB+JiPurqLyZ9V8dhxw6VXKMXacJiIi1rSi9FNiflEf9ceCwnmtpZgOzauxLaq/kGHtJE9B8zcymxwEc2XPNzGxcRMG92IaSY/SKUjMboeSbiA0lx+hG3cxGKLnBayg5xp7SBEj6gKS78vlPN50/PqcJuEvSPlVV3sz6r+SFOQ0lx9h1mgBga9Lq0Z0j4klJWwFI2hGYB7wceAHwPUnbO6e6WT2UPDOkoeQY27lRej9wf378qKRGmoD3AJ+KiCfzayvyW+YC5+fz90paBswBflxB/c2sz0qeGdJQcoxdpwkAtgdeL+laSd+X9Op8mdMEmNVYyUMTDSXH2FWagIj4g6TJwFRgd+DVwCJJL8ZpAsxqreShiYaSY+wqTUA+vRz4Zp6Xfp2kIWBLnCbArNZKnhnSUHKM7cx+eUaagOwSYK98zfbARsCDwGJgnqSNJW1Lyqt+Xb8rbmbVKHlooqHkGLtOEwCcA5wj6WfAU8D83Gu/TdIi4HbS/YgjPfPFrD5W1bIp60zJMfaaJuBv1vKek4GTe6iXmY2Tcpu7NUqO0StKzWyEksebG0qOsesVpZJ2lXSNpJvz1MQ5+bw3njarsUHtCiRpC0lLJN2df05dy3Xz8zV3S5rf4vXFeRi4bSXH2M489caK0h1I0xePzKtGPw2cGBG7Ah/Pz2HkxtMLSBtPm1lNDBFtHz06DlgaEbOApfn5CJK2AE4g7c0wBzihuWGUdBDwWKcFlxxjLxtPB7BZvmxz1kxb9MbTZjU2wJkhc4GF+fFC4MAW1+wDLImIhyJiJbAE2BeG184cC3yy04JLjrGjMfVRK0o/BFwu6VTSH4fX5cvWtqLUux+Z1cAAZ4Zs3dgVLSLub+SPGmVdK9Q/AXyGtBlPR0qOse00AaNXlALvB46JiG2AY0hz2aHNFaVOE2A2MXXSi23+HOdjQfPvkvQ9ST9rccxtszot2xNJuwLbRcTFjnGkXlaUzgcaaXi/AXw5P25rRanTBJhNTJ3MDGn+HK/l9Tet7TVJD0iannuw04EVLS5bDuzR9HwGcBXwWuBVSvsjTwa2knRVROxBG0qOsZcVpb8F3pgf7wXcnR8vBt6VZ8HsjjeeNquVAd5EXEzqHJJ/fqvFNZcDb5E0Nd88fAtweUScGREvyFtp/gXw83YbdCg7xl5WlL4HOD0n9nqCNNMFvPG0Wa0N8Gvzp0iJAA8H7gMOAZA0G3hfRBwREQ9J+gRwfX7PSRHxUK8Flxyj0sr+8eXhF7P+m738kq5mWR89c17bn8fTf3l+LfMdlhyjV5Sa2Qiri15En5Qcoxt1MxuhD+PIE17JMbZzo/RZkq6T9NOcJuDEfH5bpV2P7pZ0gaSN8vmN8/Nl+fWZ1YZgZv1UclrahpJjbGee+pPAXhGxC7ArsG+e1XIKcFpe/roSODxffziwMiK2A07L15lZTQxwZsi4KTnGdtIEREQ08g5smI8gTWO8MJ9vXv7avCz2QmDvPC3SzGpgqIOjrkqOsd3FR5OAG4HtgC8CvwAejojGptzNy1qHl7xGxCpJjwDTSLsimdkEV/JNxIaSY2wrTUBErM7ZGGeQsojt0Oqy/NNpAsxqLDr4X12VHGPbuV8AIuJh0vLV3UnZFxs9/eZUAMNpAvLrmwPPmEgfEWdFxOyImH3QpjO7qryZ9V/JQxMNJcfYzuyX50makh9vAryJlH73SuDgfFnz8tfmZbEHA1fERFjhZGZtGYpo+6irkmNsZ0x9OrAwj6tvACyKiG9Luh04X9IngZ+wJkvj2cDXJC0j9dDnVVBvM6tI/ZqxzpUcYzsbT99CyqE++vw9pPH10eefIOc3MLP6qeM0vk6VHKNXlJrZCCXPDGkoOUY36mY2Qsm92IaSY+wlTcB5ku7KO3yckzfSIOdRPyOnCbhF0m5VB2Fm/VPydL+GkmPsJU3AecDLgFcAmwBH5Ov3A2blYwFwZr8rbWbVKXm6X0PJMbZzozSAZ6QJiIhLG9dIuo40Vx1SmoBz8/uukTSlsZ1Tf6tuZlVYH2YglxxjW4uPJE3Kux6tAJZExLVNr21I2hnpsnxqXTtjm9kEV3Kyq4aSY+wqTYCknZpe/hJwdUT8ID93mgCzGltNtH3UVckxdpsmYF8ASScAzwOObbpsOE1A1pxCoPl3OU2A2QRUci+2oeQYu00TcKekI4B9gEMjovl+wmLgXXkWzO7AIx5PN6uPiGj7qKuSY+wlTcAq4FfAj3O69G9GxEnApcD+wDLgceCwSmpuZpWo44yPTpUcYy9pAlq+N896ObL3qpnZeKjj3OxOlRyjV5Sa2Qiro+R+bFJyjG7UzWyEOt4c7FTJMXadJqDp9c9Leqzp+caSLshpAq6VNLP/1TazqpS8hL6h5Bjb6ak30gQ8lhca/VDSdyLiGkmzgSmjrj8cWBkR20maB5wCvL2/1TazqtRxY4hOlRzjmD31SJ6RJiDPhvkX4COj3jIXWJgfXwjsrTw9xswmvujgqKuSY+wlTcBRwOIWc9CH0wRExCrgEWBa/6psZlUqeWFOQ8kxdpsm4A2k3Y0+3+Jypwkwq7HVMdT2UVclx9htmoA9ge2AZZJ+CTw770kKTWkCJE0GNiftVTr6dzlNgNkEVHIvtqHkGLtNE3BjRDw/ImZGxEzg8YjYLr9lMTA/Pz4YuCLquNbWbD1V8syQhpJj7DpNwDquPxv4Wu65PwTM672aZjYo60MfrOQYu04TMOqa5zQ9foI03m5mNVTHIYdOlRyjV5Sa2Qh1vDnYqZJjdKNuZiPUcRy5UyXH2HWagJwv/WRJP5d0h6QPNp0/I6cJuEXSblUHYWb9MxTR9tELSVtIWiLp7vxz6lqum5+vuVvS/Kbzh0q6Nbczl0na0jG2N6WxkSZgF2BXYN+8+cXfkqYuviwidgDOz9fvB8zKxwLgzDbKMLMJYoAzQ44DlkbELGBpfj6CpC2AE4DXAHOAEyRNzdOlTwf2jIidgVtICyLX+xi7ThMAvB84qbHrUUSsyNfMBc7N77sGmCJp+ljlmNnEMKheLCNTiiwEDmxxzT6kVewPRcRKYAlpO03lY9OchmQzWmybuTYlx9hLmoCXAG/Pq0K/I2lWvnw4TUC2PJ8b/Tu9otRsAuqkF9v8Oc7Hgg6K2rqRZiT/3KrFNS3bk4h4mtSxvJXU0O1Imk693sfY1o3SiFgN7JoXIV0saSdgY+CJiJgt6SDgHOD1tJkmICLOAs4CuGHGgeXetTCrmU5mhjR/jluR9D3g+S1e+libRbRsT3LG2PeTplvfQ0pZcjzwyXZ+ackxdjT7JSIelnQV6avBcuCi/NLFwFfy4+E0AdkMOvhaZGbjq59paSPiTWt7TdIDkqZHxP15iHZFi8uWA3s0PZ9BSlWya/79v8i/axEtxqvXpuQYu00TcCdwCbBXvuyNwM/z48XAu/IsmN2BR1pkcjSzCWqANxGbU4rMB77V4prLgbfkG4dTgbfkc78BdpT0vHzdm4E72i245Bi7ThMg6YfAeZKOAR4DjsjXXwrsDywDHgcOa6MMM5sgYnALcz4FLJJ0OHAfeSW60uY774uIIyLiIUmfAK7P7zkpIh7K150IXC3paeBXpBl5bSk5Rk2EHAgeUzfrv9nLL+lqc5o/m7Zz25/HX/3+llpugFNyjF5RamYjTISOXtVKjtGNupmNUHJelIaSY+wlTcDekm6SdLOkH0raLp/fWNIFOU3AtZJmVhuCmfXTABfmjJuSY+wlTcCZwDvyNnf/Afxjvv5wYGXeNOM04JT+V9vMqjLAmSHjpuQYe0kTEKRlq5C2rGvMRW9eFnshsHde4mpmNRARbR91VXKMvaQJOAK4VNJy4J2kqTvQtOQ1IlYBjwDTWvxOpwkwm4BK3r+zoeQY22rUI2J1HmaZAczJaQKOAfaPiBmk1aSfzZe3nSbAG0+bTTyrh4baPuqq5Bi7TROwH7BL7rEDXABclh830gQsz6kjNyftVWpmNVDHIYdOlRxjt2kC7gA2l7R9vqx5+WrzstiDgSui5P+CZoUpeWiioeQYe0kT8B7gIklDwErg3fn6s4GvSVpG6qHPq6DeZlaR9aEPVnKMYzbqEXELKfXj6PMXk7Izjj7/BDm/gZnVTx3nZneq5Bi9otTMRqjj3OxOlRyjG3UzG6GOMz46VXKMbU1phOG56j+R9O38fNucBuDunBZgo3zeaQLMaqzk1ZYNJcfYdqMOHM3IBO2nAKflXbJXkl8GctIAAAw6SURBVNIDgNMEmNVayastG0qOsd0VpTOAvwS+nJ+LtOvRhfmS5l2ynSbArMZKbvAaSo6x3TH1zwEfAZ6bn08DHs5pACDvfp0fj0gTIKmRJuDB5l+Yd+Ru7Mr93ry5a1ckLejl/XUrdzzLXh9jHs+yx6Pcp5/6TfGdsJJjbGfx0VuBFRFxY/PpFpdGG6+tOdGUJqAP/2gXjH1JJcar3PEse32MeTzLHs+YrYba6an/OXCApP2BZ5EyM34OmCJpcu6tz2BNlkanCTAzGyftpN49PiJmRMRM0urQKyLiHcCVpDQAMHKXbKcJMDMbJ53Mfhnto8CxOR3ANFJ6APLPafn8scBxvVWxLeMyzjqO5Y5n2etjzONZ9njGbDUkd6LNzMrRS0/dzMwmGDfqZmYFcaNuZlYQN+pmZgWpbZZGSS8BlkfEk5L2AHYGzo2Ihysu9xPAiY3VtJI2A06PiMMqLndr4J+BF0TEfpJ2BF4bEWeP8dZ+lf98YA5pIdn1EfG7QZSby34h8Gc0/XuNiKsHUK6AdwAvjoiTJL0IeH5EXFdRef9Ji4V6DRFxQBXlWlnq3FO/CFgtaTvSNMptgf8YQLmTgWsl7SzpLcD1wI1jvKcfvgpcDrwgP/858KEBlIukI4DrgINIaw+ukfTudb+rb2WfAvwI+Efg7/Px4UGUDXwJeC1waH7+KPDFCss7FfgMcC/wJ+Df8/EY8LMKy7WC1HZKo6SbImI3SX8PPBERn5f0k4h4xi5NFZT9JuA/Sdkp3xARywZQ5vUR8ermGCXdHBG7DqDsu4DXRcTv8/NpwH9HxEsHVPbOEfFk1WW1KLvxb6z5v/lPI2KXisu9OiLeMNY5s1bq3FN/WtKhpNWr387nNqy6UElvAE4HTgKuAr4g6QXrfFN//DE3ppHrsTvwyADKhZT64dGm54+Sk7YNwD0M4P/XtXg6783b+G/+PGAQuys8T9KLG08kbQs8bwDlWgFqO6YOHAa8Dzg5Iu7N//D/3wDKPRU4JCJuB5B0EHAF8LKKyz2WlILhJZJ+RPqQH7zut/TNb0hDTt8iNXBzgeskHQsQEZ/td4GSPp/Lehy4WdJSYLi3HhEf7HeZLZxB2od3K0knk/57/+MAyj0GuErSPfn5TOC9AyjXClDb4ZdmkqYC2+RNsqsua1JErB51blpjaKLisicDLyVlwrwrIp6uusxc7gnrej0iTqygzPnrej0iFq7r9T7W42XA3qT/5ksj4o4x3tKvcjdmTUfhzvEYfrJ6qm2jLukq4ADSt42bgf8Bvh8Rx1ZcbmMWygsjYt9BzULJ3whGewS4NSJWVFn2qHpMJeXSH8g/HEmbku6ZrM7PJwEbR8TjFZe7AXBLROxUZTlrKfvZpG9mfxYR75E0C3hpRHx7jLea1XpMffOI+ANpRsZXIuJVwJsGUO5XSbNQpufng5qFcjhp56l35OPfSR/8H0l6ZxUFSvp47qk29p69AvgF8EC+WTwIS4FNmp5vAnyv6kIjYgj4aZ7GOGhfAZ4izbyBdE/jk+NQD6uhOjfqkyVNB/6KNTdKB2HLiFhEvmGW56uvXvdb+mII2CEi3hYRbwN2JI0xv4aUMbMKbwfuyo/nk/69PA94I+nbyiA8KyIeazzJj589oLKnA7dJWippceMYQLkviYhPA08DRMSfaL35jNkz1PlG6UmkHvMPI+L6PFvg7gGUO16zUGZGxANNz1cA20fEQ5KqGlt/qmmYZR/g63kY5I48vj8If5S0W0TcBCDpVaQ53IPQ93sFbXpK0ias+Tf2EppuEputS20b9Yj4BvCNpuf3AG8bQNHjNQvlB5K+zZqY3wZcncecq1pF+6SknYAHgD0ZuehnUL3lo4FvSGrsrDWd9A2ichHx/UGU08IJwGXANpLOI+0+9rfjVBermTrfKH0WaZz55aRt9gCIiEpWOkp6NfDriPhd7qW+l9Sw3g58PCIq3bIvL1k/CPiLfOr3wPSIOLLCMl8DLCT94fpcRHwin98feGdEHLqu9/eh/A2A3Umrdhuzfu4c4Kyf3YHPAzsAGwGTgD9GxGYDKHsaKXYB10TEg2O8xQyo95j614Dnk4YFvk/aJ/XRdb6jN/9GunkF8DrgY6Ql4ysZwO40eRjkF6Rx1v9NmmZX6fS6iLg2Il4WEdMaDXo+f2nVDXouZwj4TEQ8HRE/i4hbB9WgZ18gpQi4m3SD9oh8rlKSToqI30fEf+UZLw/lHrvZmOrcqG8XEf+H1HNaCPwl8IoKy5vU1Bt/O3BWRFyU67BdVYVK2j7PQrmD1KD8mvQNa8+IqLyByXWYJukMSTdJulHS6bknOQjflfS2/E1l4HIKiEkRsToivgLsMYBiXyTpeBier34Jg7lfZAWo7Zg6eWYA8HAe9/0daeVdVSZJmpxnu+wNLGh6rcr/jncCPwD+VyPHjKRjKiyvlfOBq1lzz+IdwAUMZgrpscCmwCpJT5CGI2IQQyDA45I2Iq1o/TRwf65L1Q4DzssN+57AdyLitAGUawWoc6N+Vl4I839INy6fA3y8wvK+Dnxf0oOk2Rc/AMhZIquc/fI2YB5wpaTLSA3soHutWzQPvwCflHTgIAqOiOcOopy1eCfp2+xRpKX721DhzXhJuzU9PZ005Pcj0r+74RlAZutS2xul4yHfOJsOfDci/pjPbQ88p+oPXJ7lciBpjHcv0g3MiyPiu1WWm8s+FbgBWJRPHQy8PCLWmT6gj+VPBWYx8oZ4ZfnUJb0oIu6r6vevo9wr1/FyRMReA6uM1VbtGvVGEqm1qSK51EQjaQvgEODtVX7QJT1Kmist0rBDY5HVJOCxAc0COYI0rXEGKR3E7sCPK477pojYLT++KC/2Gog84+eQiLhgUGVaWep4o/S5YxzFi4iHIuLfqu65RcRzI2Kz/HODiNgwHxsMaEwbUoP+auBXEbEn8EpSnp8qNQ9vvXitV1Ugz/ipbJqqla92Y+pVZAS01iS9LCLuHDXWO2xAY7xPRMQTkpC0ca5P1ZtzxFoeD8oSSR8m3Yz+43BFKl4LYWWo3fBLg6SFwNGR9yTN466fqWrx0fpI0lkRsWDUWO/wP5hBjPFKupg0G+RDpHsJK4ENI2L/CstcTWpMRZqf3sgIOZCZN5LubXE6ImKg3xqsnurcqD9j67pW56x7kuYA90XeZDrnOH8b8Evgnwbdc5T0RmBz4LKIeGqs683WR7UbfmmygaSpEbEShm8e1jmeiehfyXPRlbbx+7/AB4BdSatoK8t5k9NAvI+0sOtW4OxxzMUycHntxY6MnPFz7vjVyOqizo3gZ4AfS/oGaUjgr4CTx7dKxWm5iha4SNLNFZe9kLTA7AfAfqQG7uiKy5wQ8k5Te5BivpQU/w8BN+o2pto26hFxrqQbSOOsAg6KvG+o9c14raIF2DEiXgEg6WzguorLm0gOBnYBfhIRh+Xdtr48znWymqhdo97ia/m/5kbH+m+8VtHCmjQQRMSqcUr9Ml7+FBFDklZJ2oyUO983Sa0ttWvUeebX8h0YzHZy652IOFnSUtasom3cVd+ANLZepV0k/SE/FrBJfj7I3C/j5QZJU0hbFt4IPMb69U3FelC72S+Sbm36Wj4ZuK6x+s+sNJJmAptFxC3jXBWriTr21Nfnr+W2npDU2BAlSDdJ3ahbW+rYU28sDIGRi0PWh6/lth6Q9CXSPaOv51NvB35R5S5XVo7aNepmpZN0G7BT4x5GTvJ1a0S8fHxrZnVQx4ReZqW7C3hR0/Nt8PCLtamOY+pmRZL0n6Qx9M2BOyRdl5+/Bvjv8ayb1YcbdbOJ49TxroDVn8fUzSaovPBouOPl1LvWDvfUzSYYSQuAT5BW8Q6RZ3bhVaXWBvfUzSYYSXcDr42IB8e7LlY/nv1iNvH8gjUbc5h1xD11swlG0iuBrwDXAk82zkfEB8etUlYbHlM3m3j+DbiClIV0aJzrYjXjRt1s4lkVEceOdyWsnjymbjbxXClpgaTpkrZoHONdKasHj6mbTTCS7m1xOiLCUxptTG7UzcwK4uEXswlC0keaHh8y6rV/HnyNrI7cqJtNHPOaHh8/6rV9B1kRqy836mYTh9byuNVzs5bcqJtNHLGWx62em7XkG6VmE0TTVo3N2zSSnz8rIjYcr7pZfbhRNzMriIdfzMwK4kbdzKwgbtTNzAriRt3MrCBu1M3MCvL/AWV455/1julRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#loading datasets\n",
    "train_df = pd.read_csv(r'F:\\Datasets\\titanic\\train.csv')\n",
    "test_file = r'F:\\Datasets\\titanic\\test.csv'\n",
    "test_df = pd.read_csv(test_file)\n",
    "train_df.head()\n",
    "test_df.head()\n",
    "\n",
    "#checking for na values\n",
    "train_df[train_df['Embarked'].isna()]\n",
    "\n",
    "#filling na values intrain data\n",
    "train_df['Embarked'].fillna(value = 'C', inplace = True)\n",
    "train_df['Sex'].replace({'male':0,'female':1}, inplace=True)\n",
    "train_df['Embarked'].replace({'S':0,'C':1,'Q':2}, inplace=True)\n",
    "train_df['Age'].fillna((train_df['Age'].mean()), inplace = True)\n",
    "train_df.drop(['Cabin', 'PassengerId', 'Name', 'Ticket'], axis = 1, inplace = True)\n",
    "\n",
    "train_df.head()\n",
    "\n",
    "#vizualising for null values in train data\n",
    "sns.heatmap(train_df.isnull())\n",
    "\n",
    "#data processing in test data\n",
    "test_df.fillna(test_df['Age'].mean(), inplace = True)\n",
    "test_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis = 1, inplace=True)\n",
    "test_df['Sex'].unique()\n",
    "test_df['Embarked'].unique()\n",
    "test_df['Sex'].replace({'male':0, 'female':1}, inplace= True)\n",
    "test_df['Embarked'].replace({'Q':0,'S':1,'C':2}, inplace =True)\n",
    "\n",
    "#Vizualising for missing values in testdata\n",
    "sns.heatmap(test_df.isnull())\n",
    "\n",
    "train_df.info()\n",
    "test_df.info\n",
    "\n",
    "#proceesing the data to train and test\n",
    "x_train = train_df.drop(['Survived'], axis = 1)\n",
    "y_train = train_df['Survived']\n",
    "x_test = test_df\n",
    "\n",
    "#finding shape of x_train, x_test, y_train\n",
    "x_train.shape, y_train.shape, x_test.shape\n",
    "\n",
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(x_train, y_train)\n",
    "y_pred = decision_tree.predict(x_test)\n",
    "acc_decision_tree = round(decision_tree.score(x_train, y_train) * 100, 2)\n",
    "acc_decision_tree\n",
    "\n",
    "#Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(x_train, y_train)\n",
    "y_pred = random_forest.predict(x_test)\n",
    "random_forest.score(x_train, y_train)\n",
    "acc_random_forest = round(random_forest.score(x_train, y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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