Cocomo analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arff, numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import KFold,cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV,RandomizedSearchCV\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = arff.load(open('cocomo811.arff'))\n",
    "data=np.array(dataset['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[:,0:16]\n",
    "Y=data[:,16:17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=Y.reshape(63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cocomo n_estimator=100 and max_features= 1 to 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, Y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "lab_enc = preprocessing.LabelEncoder()\n",
    "training_scores_encoded = lab_enc.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_feature=7\n",
    "MMRE_list=[]\n",
    "MdMRE_list=[]\n",
    "Pred_list= []\n",
    "for i in np.arange(1,11):   \n",
    "    clf= RandomForestRegressor(n_estimators=100,max_features=i)\n",
    "    clf.fit(X_train, training_scores_encoded)\n",
    "    Y_pred=clf.predict(X_test)\n",
    "    diff=np.absolute(y_test-Y_pred)\n",
    "    MRE=diff/y_test\n",
    "    MMRE=np.mean(MRE)\n",
    "    MdMRE=np.median(MRE)\n",
    "    P=MRE[MRE<.25]\n",
    "    Pred=(P.size/MRE.size) * 100\n",
    "    MMRE_list.append(MMRE)\n",
    "    MdMRE_list.append(MdMRE)\n",
    "    Pred_list.append(Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7971338091857509,\n",
       " 0.7678155233445535,\n",
       " 0.7518933931836663,\n",
       " 0.7749452835675603,\n",
       " 0.7588313003445126,\n",
       " 0.7688729198148886,\n",
       " 0.78671109305838,\n",
       " 0.7669305754274645,\n",
       " 0.7734761681310482,\n",
       " 0.7714228328824447]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MMRE_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7688888888888888,\n",
       " 0.7751219512195122,\n",
       " 0.7836585365853659,\n",
       " 0.8080487804878049,\n",
       " 0.7963414634146341,\n",
       " 0.8096341463414635,\n",
       " 0.8148780487804878,\n",
       " 0.8292682926829268,\n",
       " 0.8026923076923076,\n",
       " 0.8213076923076923]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MdMRE_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 5.263157894736842,\n",
       " 0.0,\n",
       " 0.0]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(1,11)\n",
    "\n",
    "plt.plot(x, MMRE_list)\n",
    "plt.plot(x, MdMRE_list)\n",
    "\n",
    "plt.legend(['MMRE','MdMRE'], loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, Pred_list)\n",
    "plt.legend(['Pred'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COCOMO n_estimator=100 to 2000 and max_features= 7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "MMRE_list=[]\n",
    "MdMRE_list=[]\n",
    "Pred_list= []\n",
    "for i in np.arange(100,1100,100):   \n",
    "    clf= RandomForestRegressor(n_estimators=i,max_features=7)\n",
    "    clf.fit(X_train, training_scores_encoded)\n",
    "    Y_pred=clf.predict(X_test)\n",
    "    diff=np.absolute(y_test-Y_pred)\n",
    "    MRE=diff/y_test\n",
    "    MMRE=np.mean(MRE)\n",
    "    MdMRE=np.median(MRE)\n",
    "    P=MRE[MRE<.25]\n",
    "    Pred=(P.size/MRE.size) * 100\n",
    "    MMRE_list.append(MMRE)\n",
    "    MdMRE_list.append(MdMRE)\n",
    "    Pred_list.append(Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7808978175154454,\n",
       " 0.7701772927661539,\n",
       " 0.7670331265707331,\n",
       " 0.7678005333915134,\n",
       " 0.7688930165767264,\n",
       " 0.7674646568513901,\n",
       " 0.7728694215267984,\n",
       " 0.7725009193040682,\n",
       " 0.7739206573869396,\n",
       " 0.7679683151012039]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MMRE_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8207317073170731,\n",
       " 0.8136585365853658,\n",
       " 0.8110569105691057,\n",
       " 0.8114634146341463,\n",
       " 0.8136461538461539,\n",
       " 0.8228589743589744,\n",
       " 0.8094773519163763,\n",
       " 0.8243365384615384,\n",
       " 0.8141869918699187,\n",
       " 0.8131615384615385]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MdMRE_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 5.263157894736842, 5.263157894736842, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(100,1100,100)\n",
    "plt.plot(x, MMRE_list)\n",
    "plt.plot(x, MdMRE_list)\n",
    "\n",
    "plt.legend(['MMRE','MdMRE'], loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAHTpJREFUeJzt3XtwXOd5HvDn3cUCC+weXgACBxRJEaSIXdH1RVI5LhnJ40aJHSZ2kulMPLbGbj2tarbjdOrUmdHEf7nueKbjqUdx24njcBJbbpvKbRO5TTWx5IukycStFVExa+vCXfAmEaL24MLbWdx39+0few4IQrgsFufsd87u85vBEFgcgi+Ojh58+M57vk9UFUREFB8J0wUQEdHWMLiJiGKGwU1EFDMMbiKimGFwExHFDIObiChmGNxERDHD4CYiihkGNxFRzHSF8UX37NmjIyMjYXxpIqK29PLLL0+p6mAjx4YS3CMjIzhz5kwYX5qIqC2JyBuNHsupEiKimGFwExHFDIObiChmQpnjJiJqxtLSEsbHxzE/P2+6lNCk02ns378fqVSq6a/B4CaiyBgfH4dlWRgZGYGImC4ncKqK6elpjI+P49ChQ01/HU6VEFFkzM/PY2BgoC1DGwBEBAMDA9v+jYLBTUSR0q6h7Qvi++NUSUSVFyr49v+5jIWlqulScGgwg39w/37TZRCRh8EdUd/7+dv4d88WAAAmByD+lqS/dNTGjnTzN1OI4iKZTOI973kPKpUKjh49im9/+9vo6+tr6mu98MIL+OpXv4qnn3460BoZ3BFVdFz0dCXw2r85iWTCXHL/8DUH//Q/ncGYU8bfPbjbWB1ErdLb24uzZ88CAD75yU/iG9/4Bj7/+c8vf15VoapIJMzNNHOOO6IKThlHhrJGQxsAcrYFoP6DhKjTfOADH8D58+dx+fJlHD16FJ/97GfxwAMP4MqVK/j+97+PEydO4IEHHsDHPvYxlMtlAMAzzzyDe++9Fw899BCeeuqpUOriiDuiiiUXv3DPgOkysH93L3pTSRRKDG5qrS/971fx2tVbgX7Nd921A1/89b/T0LGVSgXf+973cPLkSQBAoVDAt771LXz961/H1NQUvvzlL+OHP/whMpkMvvKVr+Dxxx/HY489hs985jN47rnncOTIEXz84x8PtH4fgzuCbs4toXRrHrlhy3QpSCQEOTvLETd1jLm5Odx3330A6iPuRx99FFevXsXBgwdx/PhxAMBPfvITvPbaa3jwwQcBAIuLizhx4gTOnTuHQ4cOYXR0FADwqU99CqdPnw68RgZ3BI15IZm3zQc3UJ8ueb4waboM6jCNjoyDtnKOe6VMJrP8vqriQx/6EJ588sk7jjl79mxL2hkbmuMWkcsi8nMROSsiXK81ZAUvuKMw4gaA/LCFqfICpssLpkshioTjx4/jxz/+Mc6fPw8AmJ2dRbFYxL333otLly7hwoULAPCOYA/KVm5O/qKq3qeqx0KphJYVSy6yPV24a2fadCkAgNHlG5Rlw5UQRcPg4CCeeOIJPPLII3jve9+L48eP49y5c0in0zh9+jQ+8pGP4KGHHsLBgwdD+fc5VRJBBcfFqJ2NzBNk+RWdJScicMOUKEx+d8hKIyMjeOWVV+547eGHH8ZLL730jmNPnjyJc+fOhVYf0PiIWwF8X0ReFpFTYRZE9ZFtVOa3AcDe0YMd6a7lKRwiMqvREfeDqnpVRIYA/EBEzqnqX608wAv0UwBw9913B1xm55gqL+DazOJy/3QUiAjyw9byTVMiMquhEbeqXvX+nADwXQDvX+OY06p6TFWPDQ42tN8lraHo9UvnI3Jj0pezLRRKLtR/Bp4oJO1+jQXx/W0a3CKSERHLfx/AhwG8svHfomb50xGjdtZwJXfK2RZuzVfg3GJnCYUnnU5jenq6bcPbX487nd5e40EjUyU2gO96N8q6APxXVX1mW/8qravouNjdl8Jgtsd0KXfwp24KjovhiHS7UPvZv38/xsfHMTnZvs8N+DvgbMemwa2qFwG8b1v/CjWsUHKRs63IdJT4ct5vAMWSiw/mOBVG4UilUtvaGaZTcJGpCFFVjDnlyM1vA8BAtgd7sj189J0oAhjcEfL2zXm4C5VIdZSslB/mmiVEUcDgjpDlR90jGtyjQxaKThm1WnveOCKKCwZ3hPitgLmIdZT48sMW5paqGL8+Z7oUoo7G4I6QolOGvaMHu/q6TZeyJm6qQBQNDO4IKTpuZKdJgNu/CfDRdyKzGNwRUa0pxibcSK1RspqVTmHfrl6OuIkMY3BHxJVrs5hfqkV6xA3Un+jkNmZEZjG4IyJqmyesJ29buDg5g0q1ZroUoo7F4I4If+W90aFodpT4craFxWoNl6dnTZdC1LEY3BFRcMo40N+LTE+097bwn+rkPDeROQzuiCiWon1j0ndkKAsRcJ6byCAGdwQsVmq4MFle3tsxytKpJA7293HETWQQgzsCLk/PoFLTWIy4AW9TBQY3kTEM7ggoRnyNktXywxbemJ7F/FLVdClEHYnBHQHFkotkQnB4MGO6lIbkbAvVmuLi5IzpUog6EoM7AgqOi5GBPqRTSdOlNISdJURmMbgjoOiUYzNNAgAjAxl0JYTz3ESGMLgNm1+q4vL0TKyCu7srgcODmeVlaImotRjchp2fKEMVkdyubCM520JxgsFNZAKD27C4dZT48raFK9fmMLNQMV0KUcdhcBtWcFx0JxMYGegzXcqW+IthjU2UDVdC1HkY3IYVSy4OD2bQlYzXf4rl3XA4z03UcvFKizZUdMqxm98GgLv7+9DTlWBnCZEBDG6D3PklvHVjLnbz2wCQTAhG7Sx7uYkMYHAb5M8Px2WNktVytsXgJjKAwW2QPz8cxxE3UP+B49xawI3ZRdOlEHUUBrdBBcdFbyqJ/bt7TZfSlOUblA47S4haicFtUNFxkbOzSCTEdClN8VsCeYOSqLUaDm4RSYrIT0Xk6TAL6iRxW6Nktbt2ppHt6VreL5OIWmMrI+7PAXg9rEI6zbWZRUy6C7FsBfSJCHJ2ltuYEbVYQ8EtIvsBfATAH4dbTufwuzHisF3ZRvLD9c4SVTVdClHHaHTE/TUAjwGohVhLR/GDO66tgL7RIQvXZ5cwWV4wXQpRx9g0uEXkowAmVPXlTY47JSJnROTM5ORkYAW2q0LJxY50F+wdPaZL2ZblTRVK7CwhapVGRtwPAvgNEbkM4DsAHhaR/7L6IFU9rarHVPXY4OBgwGW2nzHvUXeReHaU+G63BHKem6hVNg1uVf2Cqu5X1REAnwDwnKp+KvTK2piqouC4se4o8e3JdqM/083gJmoh9nEbMOEu4ObcUlsE93JnCYObqGW2FNyq+oKqfjSsYjpFIeaPuq+Wsy0US+wsIWoVjrgNuL3rTdZwJcHI2RZmFqt468ac6VKIOgKD24Ci42JPtgcD2Xh3lPj8zpIxrllC1BIMbgMKThn54fYYbQNAbohrlhC1EoO7xWo1xZjjYnSoPea3AWBnXwrDO9LcxoyoRRjcLfbWjTnMLlZjvUbJWkbZWULUMgzuFmu3jhJf3rZwfqKMao2dJURhY3C3WHGivTpKfLlhCwuVGt68Nmu6FKK2x+BusWLJxb5dvbDSKdOlBMpfLItLvBKFj8HdYgWnjNE2G20DWP6e+Og7UfgY3C1UqdZwYaIc+6Vc19LX3YUD/b28QUnUAgzuFro8PYvFaq3tbkz68t6j70QULgZ3C/l7M7ZbK6AvZ1u4NDWDxQr32yAKE4O7hQqOCxHgyFD7zXED9R9IlZri0tSM6VKI2hqDu4WKjouD/X1Ip5KmSwmFPwXEeW6icDG4W6hQao/NE9ZzeDCDZEI4z00UMgZ3iyxUqrg8Pdu289sA0NOVxMhAH1sCiULG4G6Ri5MzqNa0rUfcQH2em8FNFC4Gd4sU27yjxJezLbxxbRZzi1XTpRC1LQZ3ixRKLroSgpGBjOlSQpW3LagC5ye4qQJRWBjcLVJ0XBwezKC7q71PeW6YnSVEYWvvFImQolNu+/ltADjY34fuZGL5YSMiCh6DuwVmFyt489psW65RslpXMoF7hripAlGYGNwt4G+im2vzG5O+vJ1lLzdRiBjcLeCPPjthqgSo/4C6enMet+aXTJdC1JYY3C1QLLno6Urg7v4+06W0hD8lxHluonAwuFugOFHfPCGZENOltIT/m0XRYUsgURgY3C1QbPM1Slbbt6sXfd1JbmNGFBIGd8huzi6hdGu+IzpKfImEYNTmo+9EYWFwh+z2ru6dE9yA11nC4CYKxabBLSJpEfkbEfl/IvKqiHypFYW1C3+6oFNaAX0528JUeRHT5QXTpRC1nUZG3AsAHlbV9wG4D8BJETkeblntY8xxke3pwl0706ZLaSneoCQKz6bBrXX+/30p701DraqNFBwXOTsLkc7oKPH5qyByuoQoeA3NcYtIUkTOApgA8ANVfXGNY06JyBkROTM5ORl0nbGkqm2/6816hqwe7OxN8dF3ohA0FNyqWlXV+wDsB/B+EXn3GsecVtVjqnpscHAw6Dpjaaq8iOuzSx0Z3CKCvG3x0XeiEGypq0RVbwB4AcDJUKppM52yecJ6csP1xaZUObNGFKRGukoGRWSX934vgF8GcC7swtpBscPWKFktZ1tw5ytwbrGzhChIjYy49wJ4XkR+BuAl1Oe4nw63rPZQdFz0Z7qxJ9ttuhQj/B9YnOcmClbXZgeo6s8A3N+CWtpOoeRidKjzOkp8yy2BJRcfzPG+B1FQ+ORkSFQVRafcsfPbANCf6cag1cMRN1HAGNwhuXpzHuWFSsfOb/vyXLOEKHAM7pB0ekeJb9TOYswpo1ZjZwlRUBjcIfH7l3NDnR3cedvC3FIV49fnTJdC1DYY3CEpOC7sHT3Y2ZcyXYpR/uJanOcmCg6DOyRFpzMfdV9tdCgLgGuWEAWJwR2Cak0x5pQ7avOE9VjpFPbt6uVuOEQBYnCH4Mq1WSxUah23Bvd6ctxUgShQDO4Q+PO5HHHX5YYtXJycwVK1ZroUorbA4A6B31FyxJvf7XR528JitYY3pmdMl0LUFhjcISg4Lg709yLTs+mKAh1hec2SEnfDIQoCgzsERcflNMkKR4aySAhbAomCwuAO2GKlhouTM2wFXCGdSuLgQAZjDG6iQDC4A3Z5egaVmnb8o+6r5ewsR9xEAWFwB8zvVx7t8EfdV8vbFi5PzWB+qWq6FKLYY3AHrOi4SCYEhwczpkuJlNywhZoCFyZ5g5JouxjcASuUXIwM9CGdSpouJVL8m7V8EIdo+xjcARub6OzNE9YzsieDVFJQdDjiJtouBneA5pequDzNjpK1pJIJHN6TXX44iYiax+AO0PmJMlQ7d1f3zeSGLXaWEAWAwR0gv6OEwb22vJ3F+PU5zCxUTJdCFGsM7gAVJ1x0JxMYGegzXUok+T/QxiY4z020HQzuABVLLu4ZyqIrydO6Fj+4Oc9NtD1MmAAVnTLyNlcEXM+B/j6kUwnOcxNtE4M7IO78Et66MYdRzm+vK5kQjA5Z7OUm2iYGd0D8/mSuCrixnG1xGzOibWJwB8Rf+Y4P32wsP5zFhLuAG7OLpkshii0Gd0AKjou+7iT27eo1XUqkjS4/+s7OEqJmbRrcInJARJ4XkddF5FUR+VwrCoubouNi1LaQSIjpUiLNn0riDUqi5jUy4q4A+F1VPQrgOIDfFpF3hVtW/BRKZeS4x+Sm9u5Mw+rpYksg0TZsGtyq+raq/q33vgvgdQD7wi4sTq7NLGKqvMD57QaICB99J9qmLc1xi8gIgPsBvBhGMXHlt7fxUffG5GwLY44LVTVdClEsNRzcIpIF8OcAfkdVb63x+VMickZEzkxOTgZZY+QV2VGyJTk7i+uzS5gsL5guhSiWGgpuEUmhHtp/qqpPrXWMqp5W1WOqemxwcDDIGiOvUHKxszeFIavHdCmxsLypQomdJUTNaKSrRAD8CYDXVfXx8EuKn6LjImdnUT9VtJncMDtLiLajkRH3gwD+IYCHReSs9/ZrIdcVG6qKQsnl/PYW7Mn2YCDTzc4SoiZ1bXaAqv41AA4l1zHhLuDWfIXz21uUsy0UJxjcRM3gk5PbxM0TmpOz69uYsbOEaOsY3NvEVsDm5IYtzCxW8daNOdOlEMUOg3ubCiUXe7I96M90my4lVpY7S3iDkmjLGNzbVHRc5If5qPtW+YtNFdgSSLRlDO5tqNUUYxNlTpM0YWdvCnt3ppeXwyWixjG4t+GtG3OYXaxy84Qmjdpcs4SoGQzubfA7SrhdWXPydhZjE2VUa+wsIdoKBvc2FJY7SjjH3YycbWGxUsMb0zOmSyGKFQb3NhQdF/t29cJKp0yXEkv+Q0vsLCHaGgb3NhSdMkfb23BkKAsRbmNGtFUM7iZVqjVcmCgvL5hEW9fX3YUDu/t4g5JoixjcTbo8PYvFag25IQb3duRsi4tNEW0Rg7tJ3DwhGPnhLC5NzWChUjVdClFsMLibVCi5EKnP01LzcraFSk1xaYqdJUSNYnA3aWzCxchABulU0nQpsXa7s4Q3KIkaxeBuUn3zBI62t+vQngySCeE8N9EWMLibML9UxeXpWa5REoCeriQO7cmws4RoCxjcTbg4OYNqTRncAcnbFh/CIdoCBncT2FESrJxt4c1rs5hbZGcJUSMY3E0oOi5SScHIQMZ0KW0hP5yFKnB+gjcoiRrB4G5C0XFxeE8W3V08fUFY3lSB0yVEDWHyNKHguBhlR0lgDvb3obsrwXluogYxuLdoZqGCK9fmuHlCgLqSCRwZzC6vb05EG2Nwb9GYNw/LxaWClR9mZwlRoxjcW7TcUcIRd6BytoW3b87j1vyS6VKIIo/BvUXFkot0KoED/X2mS2kr/lOo3DyYaHMM7i0qOC6ODGWRTIjpUtqK/zBTocSWQKLNMLi3qOi4fGIyBPt29SLTneQ8N1EDGNxbcHN2Cc6tBc5vhyCREIzaFjtLiBqwaXCLyDdFZEJEXmlFQVFWnPB2dWdHSSjytoWxCQY30WYaGXE/AeBkyHXEgj8a5Ig7HKN2FlPlRUyVF0yXQhRpmwa3qv4VgGstqCXyio4Lq6cLe3emTZfSlm5vqsBRN9FGApvjFpFTInJGRM5MTk4G9WUjpVCqP+ouwo6SMPi/yXBTBaKNBRbcqnpaVY+p6rHBwcGgvmxkqCqKjsulXEM0aPVgV18KRa4SSLQhdpU0aKq8iOuzS2wFDJGIIGdbHHETbYLB3SA+6t4aOTuLguNCVU2XQhRZjbQDPgng/wLIi8i4iDwaflnR43eUjDK4Q5W3LbjzFZRuzZsuhSiyujY7QFUfaUUhUVd0XPRnurEn2226lLZ2+9F3F3t39hquhiiaOFXSoPqj7uwoCZsf3GMOb1ASrYfB3YB6R0mZ89stsDvTjSGrh9uYEW2Awd2AqzfnUV6o8FH3FsnZ3FSBaCMM7gb47WlsBWwNP7hrNXaWEK2Fwd0A/9f23BCDuxXyw1nML9Vw5fqs6VKIIonB3YCi42J4Rxo7+1KmS+kI/m82Rd6gJFoTg7sBRcfl/HYLjdpcbIpoIwzuTVRrijGnjLy3JyKFL9vThX27ermpAtE6GNybePPaLBYqNT4x2WL5YXaWEK2Hwb0Jbp5gRs62cGGyjKVqzXQpRJHD4N7EmOOvUcKpklbKD2exVFW8MT1juhSiyGFwb6LguLi7vw993Zsu60IBur1mCTtLiFZjcG/CX6OEWuuewSwSAj76TrQGBvcGFis1XJyc4ROTBqRTSYwMZLipAtEaGNwbuDQ1g0pNuV2ZIVyzhGhtDO4N+KHBEbcZuWELl6dnML9UNV0KUaQwuDdQdFwkE4LDgxnTpXSkvG2hpsCFSd6gJFqJwb2BQsnFyEAferqSpkvpSP5NYU6XEN2Jwb2BouNyftugkT0ZpJLClkCiVRjc65hbrOKNa7Oc3zYolUzgnsEsR9xEqzC413FhsgxVPupuGjtLiN6Jwb0Of40SLudqVn7Ywvj1OZQXKqZLIYoMBvc6io6L7mQCB/v7TJfS0UaH6jcoxzjqJlrG4F5HwXFxz1AWXUmeIpP8m8OcLiG6jam0jmLJ5eYJEXBgdx/SqQQ7S4hWYHCvwZ1fwtWb85zfjoBEQpCzLYxNcMRN5GNwr8HfpJYdJdGQsy1uY0a0AoN7DVyjJFpydhYT7gKuzyyaLoUoEhoKbhE5KSIFETkvIr8XdlGmFUou+rqT2Ler13QphNs/QHmDkqhu0+AWkSSAPwDwqwDeBeAREXlX2IWZVHRcjNoWEgkxXQqBnSVEqzUy4n4/gPOqelFVFwF8B8BvhluWWUWnzI6SCBnekYaV7lq+90DU6RrZSHEfgCsrPh4H8PfCKObX/+NfG197WQFMlRc4vx0hIoK8beGpvx3HTy5Omy6HaF27+7rx3//5idD/nUaCe635An3HQSKnAJwCgLvvvrupYu4ZzGCxWmvq7wbp3XftwK++Z6/pMmiFf/bBe/Ddn46bLoNoQzvSqZb8O6L6jgy+8wCREwD+tar+ivfxFwBAVf/ten/n2LFjeubMmSDrJCJqayLysqoea+TYRua4XwIwKiKHRKQbwCcA/MV2CiQiouZtOlWiqhUR+RcAngWQBPBNVX019MqIiGhNjcxxQ1X/EsBfhlwLERE1gE9OEhHFDIObiChmGNxERDHD4CYiihkGNxFRzGz6AE5TX1RkEsAbgX/h1toDYMp0ERHBc3Enno878Xzctp1zcVBVBxs5MJTgbgcicqbRp5jaHc/FnXg+7sTzcVurzgWnSoiIYobBTUQUMwzu9Z02XUCE8FzciefjTjwft7XkXHCOm4goZjjiJiKKmY4MbhE5ICLPi8jrIvKqiHzOe71fRH4gImPen7u910VE/oO3WfLPROQBs99B8EQkKSI/FZGnvY8PiciL3rn4b96SvhCRHu/j897nR0zWHQYR2SUifyYi57xr5ESHXxv/yvv/5BUReVJE0p10fYjIN0VkQkReWfHalq8HEfm0d/yYiHx6OzV1ZHADqAD4XVU9CuA4gN/2NkD+PQA/UtVRAD/yPgbqGyWPem+nAPxh60sO3ecAvL7i468A+H3vXFwH8Kj3+qMArqvqEQC/7x3Xbv49gGdU9V4A70P9vHTktSEi+wD8SwDHVPXdqC/t/Al01vXxBICTq17b0vUgIv0Avoj6to/vB/BFP+yboqod/wbgfwH4EIACgL3ea3sBFLz3/wjAIyuOXz6uHd4A7PcuvocBPI36dnVTALq8z58A8Kz3/rMATnjvd3nHienvIcBzsQPApdXfUwdfG/6es/3ef++nAfxKp10fAEYAvNLs9QDgEQB/tOL1O47b6lunjriXeb/K3Q/gRQC2qr4NAN6fQ95ha22YvK91VYbuawAeA+Bv+DkA4IaqVryPV36/y+fC+/xN7/h2cRjAJIBveVNHfywiGXTotaGqbwH4KoA3AbyN+n/vl9G514dvq9dDoNdJRwe3iGQB/DmA31HVWxsdusZrbdGOIyIfBTChqi+vfHmNQ7WBz7WDLgAPAPhDVb0fwAxu/xq8lrY+H96v878J4BCAuwBkUJ8OWK1Tro/NrPf9B3peOja4RSSFemj/qao+5b3siMhe7/N7AUx4r48DOLDir+8HcLVVtYbsQQC/ISKXAXwH9emSrwHYJSL+Dkkrv9/lc+F9fieAa60sOGTjAMZV9UXv4z9DPcg78doAgF8GcElVJ1V1CcBTAH4BnXt9+LZ6PQR6nXRkcIuIAPgTAK+r6uMrPvUXAPy7vZ9Gfe7bf/0feXeMjwO46f+aFHeq+gVV3a+qI6jfdHpOVT8J4HkAv+Udtvpc+Ofot7zj22ZEpaolAFdEJO+99EsAXkMHXhueNwEcF5E+7/8b/3x05PWxwlavh2cBfFhEdnu/xXzYe605pif9Dd1oeAj1X1N+BuCs9/ZrqM/F/QjAmPdnv3e8APgDABcA/Bz1O+zGv48QzsvfB/C09/5hAH8D4DyA/wGgx3s97X183vv8YdN1h3Ae7gNwxrs+/ieA3Z18bQD4EoBzAF4B8J8B9HTS9QHgSdTn95dQHzk/2sz1AOCfeOflPIB/vJ2a+OQkEVHMdORUCRFRnDG4iYhihsFNRBQzDG4iophhcBMRxQyDm4goZhjcREQxw+AmIoqZ/w+zH7MPwTBUQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, Pred_list)\n",
    "plt.legend(['Pred'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_grd={\n",
    " 'max_features': [1,2,3,4,5,6,7,8,9,10],\n",
    "  'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   22.0s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:  3.2min finished\n",
      "C:\\Users\\Deepak\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "clf= RandomForestRegressor()\n",
    "grd_search=GridSearchCV(estimator = clf, param_grid = params_grd, \n",
    "                          cv = 10, n_jobs = -1, verbose = 2)\n",
    "grd_search.fit(X_train, training_scores_encoded)\n",
    "best_grid = grd_search.best_estimator_\n",
    "\n",
    "Y_pred=best_grid.predict(X_test)\n",
    "diff=np.absolute(y_test-Y_pred)\n",
    "MRE=diff/y_test\n",
    "MMRE=np.mean(MRE)\n",
    "MdMRE=np.median(MRE)\n",
    "P=MRE[MRE<.25]\n",
    "Pred=(P.size/MRE.size) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.768212596049101\n",
      "0.8173846153846154\n",
      "5.263157894736842\n"
     ]
    }
   ],
   "source": [
    "print(MMRE)\n",
    "print(MdMRE)\n",
    "print(Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 9, 'n_estimators': 200}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grd_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Random Hyperparameter Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "Random_grid={'bootstrap': [True, False],\n",
    " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],\n",
    " 'max_features': [1,2,3,4,5,6,7,8,9,10],\n",
    " 'min_samples_leaf': [1, 2, 4],\n",
    " 'min_samples_split': [2, 5, 10],\n",
    " 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 80 candidates, totalling 800 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    7.8s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   25.0s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done 800 out of 800 | elapsed:  2.6min finished\n",
      "C:\\Users\\Deepak\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "clf= RandomForestRegressor()\n",
    "random_grd_search=RandomizedSearchCV(estimator = clf, param_distributions = Random_grid, n_iter = 80, cv = 10, \n",
    "                              verbose=2, random_state=42, n_jobs = -1)\n",
    "random_grd_search.fit(X_train, training_scores_encoded)\n",
    "best_grid = random_grd_search.best_estimator_\n",
    "\n",
    "Y_pred=best_grid.predict(X_test)\n",
    "diff=np.absolute(y_test-Y_pred)\n",
    "MRE=diff/y_test\n",
    "MMRE=np.mean(MRE)\n",
    "MdMRE=np.median(MRE)\n",
    "P=MRE[MRE<.25]\n",
    "Pred=(P.size/MRE.size) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7578352266691194\n",
      "0.8039583333333333\n",
      "5.263157894736842\n"
     ]
    }
   ],
   "source": [
    "print(MMRE)\n",
    "print(MdMRE)\n",
    "print(Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 800,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 9,\n",
       " 'max_depth': 70,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_grd_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Regression Tree Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf= DecisionTreeRegressor(max_depth=30, min_samples_split=20)\n",
    "clf.fit(X_train, training_scores_encoded)\n",
    "Y_pred=clf.predict(X_test)\n",
    "diff=np.absolute(y_test-Y_pred)\n",
    "MRE=diff/y_test\n",
    "MMRE=np.mean(MRE)\n",
    "MdMRE=np.median(MRE)\n",
    "P=MRE[MRE<.25]\n",
    "Pred=(P.size/MRE.size) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7569677376178159\n",
      "0.8394062078272605\n",
      "5.263157894736842\n"
     ]
    }
   ],
   "source": [
    "print(MMRE)\n",
    "print(MdMRE)\n",
    "print(Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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