Python/machine_learning/Random Forest Classification/Random Forest Classifier.ipynb
2018-10-27 08:12:16 +05:30

197 lines
45 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
" from numpy.core.umath_tests import inner1d\n"
]
}
],
"source": [
"# Importing the libraries\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import confusion_matrix\n",
"from matplotlib.colors import ListedColormap\n",
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Importing the dataset\n",
"dataset = pd.read_csv('Social_Network_Ads.csv')\n",
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Splitting the dataset into the Training set and Test set\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"# Feature Scaling\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[63 5]\n",
" [ 3 29]]\n"
]
}
],
"source": [
"# Fitting classifier to the Training set\n",
"# Create your classifier here\n",
"classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\n",
"classifier.fit(X_train,y_train)\n",
"# Predicting the Test set results\n",
"y_pred = classifier.predict(X_test)\n",
"\n",
"# Making the Confusion Matrix\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"print(cm)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXuYHGWV8H+nZ5JJSGISBsgFCMl8kiEKGgTRIHyJIIgX\nFhV1wairLkbddVXQ9ZZlvaxZddeV9bJ+bgR1lSwoImoQVIhMBI0gYDRiQsAAAZJMyECGTEg6mZnz\n/VHVmb681VM1VdVVPXN+z5Mn3dXVVeft7jnnfc857zmiqhiGYRhGIWsBDMMwjHxgBsEwDMMAzCAY\nhmEYPmYQDMMwDMAMgmEYhuFjBsEwDMMAzCCMCURkiYg8lrUczULan5eIfF1ELi97/h4R6RaRPhFp\n9//vSPB+R4rIJhGZmNQ1q65/v4icmfS5WSAed4vICVnLkgVmEDJCRB4WkX3+H/8OEfm2iEzOWq64\niIiKyF5/XH0isrvB9w+lzEXkNBG5SUR2i8iTInKXiLy9ETKq6rtV9V98OcYBXwTOVdXJqtrj/78l\nwVt+FPi2qu4TkfvKvpsBEdlf9vzjIxxPp6renvS5jUBErhaRT5aeq7cx64vApzITKkPMIGTL+ao6\nGVgInAx8LGN5kuL5vlKbrKrTor5ZRFrTEKrs+ouAXwJrgWcD7cB7gFeked8AZgATgPviXsj1uYlI\nG/A3wNUAqvrc0ncD3A68t+y7+tcw1xwD/Ag4V0SOylqQRmMGIQeo6g7g53iGAQAReZWI/F5EnhaR\nR8tnMSIy15+J/42IbBWRXSKyvOz1if6K4ykR+TPwwvL7icgCEenyZ8f3ichflb32bRH5mojc7M8a\nfy0iM0XkP/3rbRKRk0cyThF5p4g86M/IfyIis8teUxH5exF5AHjAP3aCiNzin3+/iLyx7PxXisif\nRWSPiDwuIh8SkUnAzcDsslnv7BpB4N+B/1HVz6vqLvW4R1Xf6DgXEfmoiPzFv9efReS1Za89W0TW\nikiv/z18zz8uInKFiOz0v8MNInJi2Wf8GRGZD9zvX2q3iPyy7LN4tv+4TUS+4H/P3eK5myb6ry0R\nkcdE5CMisgP4lkP8FwG7VTWUC0xELhGRX4nIl0XkSeCfROR4EbnN/x52ich3RWRq2XseE5El/uPP\niMg1/sx7j4j8SUReMMJzTxWR9f5r14rIdeV/B1Vyz/flLn0P/1v22nNE5FZf/k0icqF//O+AvwY+\n7v9WbgBQ1WeA9cA5YT6zUYWq2r8M/gEPAy/zHx8DbAC+VPb6EuAkPKP9PKAbeI3/2lxAgW8AE4Hn\nA0Vggf/65/Bmf4cDxwJ/Ah7zXxsHPAh8HBgPnAXsATr9178N7AJOwZu5/hJ4CHgr0AJ8BritzrgU\neLbj+Fn+dV8AtAFfAX5V9b5bfJknApOAR4G3A614K6hdwHP887cDZ/qPpwMvKPvcHqsj32HAAPDS\nOudUXAN4AzDb/y7+GtgLzPJfuwZY7r82ATjDP/5y4B5gGiDAgrL3fBv4TNV32er6DIErgJ/4n8sU\nYDXw2TI5+4HP+5/pRMdY/h74acA4u4BLqo5d4l/zPf73PRGYD5zt/16OAn4NfKHsPY8BS/zHnwH2\n+eNvwTO+d0Q91x/PY8B78X6zbwAOAp8MGMt1wEfKvoeX+McnA4/j/X5b8X7XPQz93q92XRP4GvBv\nWeuJRv+zFUK2/EhE9uApvp3AJ0ovqGqXqm5Q1UFV/SOe4llc9f5Pqeo+Vf0D8Ac8wwDwRmCFqj6p\nqo8CXy57z4vx/kg+p6oHVPWXwI3AxWXn3KDejHk/cAOwX1W/o6oDwPfwlHM97vVXH7tFpHTvpcA3\nVfVeVS3iuccWicjcsvd91pd5H/Bq4GFV/Zaq9qvq74Hr8RQDeMrhOSLyLFV9SlXvHUamEtPxlMb2\nkOejqtep6jb/u/ge3grmtDI5jgNmq+p+Vb2j7PgU4ARAVHWjqoa+J3irDGAZcKn/uewB/hW4qOy0\nQeATqlr0P7dqpuEZ/ChsVdX/p6oD/u9rs6qu8X8vO/GMVPVvsZy1qvpz//fyXcpWvhHOfQkwqKpf\nVdWDqnodnoEN4iCecZ3lfw+/9o9fAGz2f7/9qnoPnkvo9cN8BnvwPrsxhRmEbHmNqk7Bm+mdABxR\nekFEXuQv058QkV7g3eWv++woe/wMnqIHbzb7aNlrj5Q9ng08qqqDVa8fXfa8u+zxPsfz4YLfL1DV\naf6/95Xd95AcqtqHN1Mrv2+5zMcBLyozLLvxjMpM//ULgVcCj/gum0XDyFTiKTwlOivk+YjIW33X\nRUmOExn6Lj6MtwK4Szz32zv88f0S+CrwX8BOEVkpIs8Ke0+fI/FWNPeU3ftn/vEST/iGO4in8AxT\nFMq/B8RzGX7fd809jbfCqf4tllP9u5w0gnNn460QAuWq4oN4K4m7fffc3/jHjwNeUvU7+muG//6n\nAA1NiMgDZhBygKquxfsj+0LZ4f/FcxUcq6pTga/jKZ4wbMdzFZWYU/Z4G3CsiBSqXn88othR2Yb3\nxwmA7+9vr7pveendR/Fmj9PK/k1W1fcAqOrvVPUCPBfGj4DvO65Rg3r+4XV4BmVYROQ4PNfce4F2\n9YLkf8L/LlR1h6q+U1VnA+8Cvlby/6vql1X1FOA5eG6XfwxzzzJ24Rng55Z9BlPVCwgfGtIw1/ij\nf+8oVF/z83guyZNU9VnA2wj/Wxwp26mcLEDlb7oCVd2uqpeo6iw8N9lKEZmH9zta4/gdvbf01oBL\nLsBbdY8pzCDkh/8EzhGRkttnCvCkqu4XkdOAN0W41veBj4nIdBE5BviHstfuxJuJfVhExvkBvvOB\na2OPoD7XAG8XkYXiZb78K3Cnqj4ccP6NwHwReYsv5zgReaF4AfHxIrJURKaq6kHgabxZP3irmfby\noKeDDwNvE5F/FJF2ABF5voi4PoNJeErjCf+8t+OtEPCfv8H/jMGbjSsw6Mv6IvHSSvcC+8tkDIW/\nivsGcIX4GS8icrSIvDzCZe4CpolItXKNwhS8MfSKyLHAh2JcKyx3AK3i7dFo9QPBpwSdLCJvLBvj\nbrzvYQBvUvVcEXlT2e/oNBHp9M/tBjqqrjURz3V1a8Jjyj1mEHKCqj4BfAf4Z//Q3wGf9mMM/8zQ\nDDgMn8JzzzwE/ALPN1u6zwE8A/AKvBno14C3quqmuGOoh6reClyOFwfYDvwfKn3h1efvAc71z9mG\n51ooBU8B3gI87Lsw3o3nTsIfxzXAFt9FUJNlpKq/wQtyn+Wf9ySwErjJce6fgf/AW1V04wX6f112\nyguBO0WkD0/5vF+9PQTPwlPmT+F9Fz14QdOofAQvCeC3/lhvBTrrv6VC/gN4q883j+DeJT6BFzPp\nxRvj9TGuFQo/zvRavO/2Kby42E14KxUXLwJ+JyJ7gR8Cf6+qW1W1Fy9o/Wa8390O4LMM/Y6uBJ4v\nXgbdD/xjrwFuUdVuxhiiag1yDGM0IyJH4mWdnRwQeG4KROQe4D9V9bvDnjzyewjwO+Atqroxrfvk\nFTMIhmHkEt+duRFvdfU3eNly8/xMJyMFxuIuRMMwmoMFeGnOk4C/ABeaMUgXWyEYhmEYgAWVDcMw\nDJ+mchmNmzJOJxwxIWsxDGPU0Ffs45Q9yRbZvWdKHy2FFiaOS6XatjEC+h7u26WqRw53XlMZhAlH\nTODUT56atRiGMWpY+1AXd69N9m9q3JldTJ40hYUz61WsMBpJ19u6Hhn+LHMZGYZhGD5mEAzDMAzA\nDIJhGIbh01QxBMMwjCyY3DKZi+ZcxKyJsyjkdB49yCDb923n2q3X0jfQN6JrmEEwDMMYhovmXMSJ\nx5xI25Q2vOoW+UNVad/TzkVcxJUPXTmia+TT1BmGYeSIWRNn5doYAIgIbVPamDUxdKuPGswgGIZh\nDEOBQq6NQQkRieXSyswgiMgEEblLRP7gd5r6VFayGIZhGNmuEIrAWar6fLxmFOeJyIszlMcwDCPX\n3L7mds578Xmc+8JzWfmllYlfPzODoB6lUPg4/59V2jMMw3AwMDDApz/6ab5x7Te48dc38tMbfsqD\n9z+Y6D0yjSGISIuIrAd24nUoutNxzjIRuVtE7j6452DjhTQMw4jIlB+spuPks5h/1AI6Tj6LKT9Y\nHfuaf7z3j8yZO4dj5x7L+PHjeeVrXsmam9ckIO0QmRoEVR1Q1YXAMcBpInKi45yVqnqqqp46bsq4\nxgtpGIYRgSk/WM3Myy5n3GPbEFXGPbaNmZddHtsodG/vZtbRQxlEM2fPpHt7sl0+c5FlpKq7gduA\n87KWxTAMIw5HrriCwr79FccK+/Zz5IorMpIoPFlmGR0pItP8xxOBc4BUG70bhmGkTevj2yMdD8uM\nWTPYXnaNHdt2MGPWjFjXrCbLFcIs4DYR+SNeU+tbVPXGDOUxDMOITf/R7o1hQcfDctLJJ/HIQ4/w\n2COPceDAAW760U2cdd5Zsa5ZTWalK1T1j8DJWd3fMAwjDZ5YfikzL7u8wm00OHECTyy/NNZ1W1tb\nufyzl/O3b/xbBgcHufDiCzn+hOPjilt5j0SvZhiGMcbZ8/rzAS+W0Pr4dvqPnsUTyy89dDwOi89Z\nzOJzFse+ThBmEAzDMBJmz+vPT8QANJpcZBkZhmEY2WMGwTAMwwDMIBiGYRg+ZhAMwzAMwAyCYRiG\n4WMGwTAMo0n4+Ps+zukLTuf8M9PJYDKDYBiG0SS89qLX8o1rv5Ha9c0gGIZhJMzqzas563/OYsF/\nLeCs/zmL1Zvjl78GeOHpL2Tq9KmJXMuFbUwzDMNIkNWbV3P5bZezv98rXbGtbxuX33Y5AOfPz/dm\nNVshGIZhJMgV6644ZAxK7O/fzxXrrPy1YRjGmGJ7n7vMddDxPGEGwTAMI0FmTXaXuQ46nifMIBiG\nYSTIpYsuZULrhIpjE1oncOmieOWvAS5bdhkXv+JiHnrwIRY/bzE/uPoHsa9ZjgWVDcMwEqQUOL5i\n3RVs79vOrMmzuHTRpYkElL+48ouxr1EPMwiGYaRCd183W57aQnGgSFtLGx3TO5gxOdmWj3nl/Pnn\n5z6jyIUZBKOpGQ1KZzSMoZpif5H7e+5nUAe95wPec6DpxzaaMYNgNIykFV93X3fTK53RMAYX+/v3\no2jFsUEdZMtTW5pyXIMMoqqISNai1EVVGWRwxO83g2A0hDQU35anthy6Xol6SiePM/GoY2gWqo1B\nieJAscGSJMP2fdtp39NO25S23BoFVaW4p8j2fSNPbzWDYDSENBRfkHJxHc/rTDzKGPLKqqO6Wd6x\nha1tReYU2xgQEMRpFNpa2jKQMD7Xbr2Wi7iIWRNnUchpcuYgg2zft51rt1474muYQTAaQhqKr62l\nzfl+l9LJ60w8yhjyyKqjulnWeT/PtHif7SMTiqAwTloZYKDiMy9IgY7pHVmJGou+gT6ufOjKrMVI\nnXyaOmPUEaTg4ii+jukdFKTyJxykdKIapO6+btY9uo6uh7tY9+g6uvu6RyxnPaKMIY8s79hyyBgc\nQqBf++ls7zz0/ba1tNHZ3tnUbrCxgK0QjIbQMb2jwmUD8RVfSbmEiQtEmYk30r0UZQx5ZGub26Aq\nyozJM2rGkXUcJ+v75x0zCEZDSEvxuZSOiygGqdHupbBjyCNzim2em6gKoTbwmnUcJ+v7NwNmEIyG\nkaXii2KQkoh3jJWZ6IotHRUxBAAUJoybUHNu1nGcrO/fDJhBMMYMYQ1S3EBv081Eu7thyxYoFqGt\nDTo6YEY4OZfu9M4rzzLaOr5IW2vtZ5V1RlXW928GzCAYRhVx4x15n4mufajr0OOLNwD33w+DvrzF\novccIhmFkmEAGHdml/O8rDOqsr5/M5CZQRCRY4HvADMABVaq6peykscwSsSNd6Q5E03KFTW4ohXO\nOAPWrYPBKrkGB70VQ0iDEJY0Egua6f7NQJYrhH7gg6p6r4hMAe4RkVtU9c8ZymQYQLx4R1oz0SRd\nUYXl/UAX/V1wzUmw/GzYOhXm9MKKNbB0QzrGq7O9M7PYSrNndDWCzAyCqm4HtvuP94jIRuBowAxC\nEzFag6dxxpXWTDQpV9TieUsOPf7yaV0sfxk8M957/sg0WHY+PDERLlvcFep6g2uX1BwLKm7X2d7J\nomMXhZY1aZo5o6sR5CKGICJzgZOBOx2vLQOWAbS1m68vTzRd8DQkcceV1kw0DVfUJ89t5ZnW/opj\nz4z3ji+ed8aw7y+PR5Qz2orbjRUyNwgiMhm4HviAqj5d/bqqrgRWAkyZN8VdMcvIhCRmrFFm4o1a\njSQxrjRmomm4onqrjMFwx8My2orbjRUyNQgiMg7PGKxS1R9mKYsRnbgz1igz8UauRqKOa/OuzWzr\n23bo+ezJs5l/xPxEZYJ0XFFRjMwdW+9wX6QqbfWiabBq4egqbjdWyDLLSICrgI2qmm5fOCMV4s5Y\no8zEG5nKGWVc1cYAOPQ8jlE4+zfdXHL9Fo7qKbKzvY0rL+xgzenJu6LaJ7bXyF86Xs7ah7poGYTJ\nByrP++BvqElb/fpP4dEjW7n9mOSL243WmFVeyHKF8BLgLcAGEVnvH/u4qt4U9Ia+Yl+gz9JoPAoU\nCoUR/9FHmYk3clNRlJm4S5mWjo/UIJz9m24+9O37mXDAu//MniIf+ra3GlpzerKuqJ59Pc7j2/Zs\nY/ueyrEd/KyfqlrOXbVpq5MOwneu6+e8z5xgDZGajCyzjO4AR8GTOpyyZzJ3rz01JYmMqBQWd8VK\nI4wyE2/kpqKs0xPf/L2NTKiaiU84MMhbv7/p0CqhnOpZc7G/GPiHtXjekopJlULgX2FN9pArxlx0\nG+RjdruL28Uh7xv+RgOZB5WN5ibOH32UmXijNxVlmZ44p9d9/JjdtT5516wZPEUft69XoU7a6SFj\n0dbmNAqPTUu+q5iVnkgfMwhGZkSZiWc9aw9i9uTZTrfR7MmzR3zNrVPh13NqN4ud+WitknXNmhFv\n5RSU71++D+GOrXfQP1ibUdTa0soZc9xppxVu246OyhgCsHccfPrltcXt4mKlJ9LHDIKRKVFm4nnc\nVFSKEySZZfSmC2H9TNhXtlnsnefDq/bOqjk37qzZZQzqHS9RvnoY/P6Ciiyjd7+iyI0nt7EwlATh\nsdIT6WMGwTBiMv+I+Ymmmd47r3YmvG88rJ7YQ/WcP+6seSTvL19hrH2oy6t5VFb36NqTupgc6u7R\nyOsqcTRhBsEwckaUWX/cWXOzzbrzuEocTZhBMMY0ecxrjzJrjztrtlm3UY4ZBGPM0t3XzaZdmw7t\nqC0OFNm0axOQbV571Fl73FlzXmfdeTTWox0zCMaY5YEnH6gpr6AoDzz5QKaKZzTM2nv37XZuIi2P\nP9TDNqFlgxkEI3GaZWY30gybRpDXWXsYDt6+xHm83r6GamwTWjaYQTASZSzO7JrFADYTtgktGwpZ\nC2CMLurN7PJGi7REOu6iZABLiqpkALv7uhORcawSlPZqm9DSxQyCkShp9xNe9+g6uh7uYt2j62Ir\n3fnt7r0DQcddNJMBbCY6pndQkEr1lOd02NGCuYyMRGmGfsIlkgjejgbXRh5dXqMhsN6MDGsQROQf\ngKtV9akGyGPkmapGKBcfDtfQVXGKq3pm1JmdS0HlNcjY7PV18hzzaebAerMSZoUwA/idiNwLfBP4\nuapaK8uxRnd3TSOUVT8qsGpjZ0XZgnFndjGubSKDOjiimV2Qgqop4OYTZyaehDLM607fsLP+vBpa\nIxuGNQiq+k8icjlwLvB24Ksi8n3gKlX9S9oCGjlhy5aKipaA93zLlgqDANDW2sbCmeFKm1V3Bjvh\nPQMMttYqqCCiBICrSap3culaeXFtRDF0o8HlZSRHqBiCqqqI7AB2AP3AdOAHInKLqn44TQGNnBDQ\nCCXweAhcncGejqjfvU6sIyMpZZg310YUQ9fsLi8jWcLEEN4PvBXYBVwJ/KOqHhSRAvAAYAZhLBDQ\nCIW2WsURtEu1mm99j5rOYHN6vXLPYYmziWy0KsNGFseriyPmtG1e/Msa6RFmhTAdeJ2qPlJ+UFUH\nReTV6Yhl5A5HIxQKBe94GUG7VJ30dtUcWrEGlp0Pz4wvu40UKEjBqfyn9rdyzQfXOZvRD0dUZZjH\nbBwX9Qydawxx2qAG4og5rVwNVxzRHfr7MRpP3X0IItICvL7aGJRQ1Y2pSGXkjxkzoLNzaEXQ1uY9\nnxHjj9uxuli6AVbe3MJx+9tAPSXW2d7J8YcfX5OX3jIIX1zdz8yeIgU8l9Ol39rIMavXhhvS5Bl0\ntnceWhGU7uVShs20AS0oh799YrtzDACLjl3EkrlLWHTsomSMnCPmNOkgXHK97c/IM3VXCKo6ICJ/\nEJE5qrq1UUIZOaWqEUpYgmrYXHw4rFztKYoSe8fBTfMG2No2gEBNG8jymeznbiryjj9UXnPSQfjM\nrcorXhpuNh/W/99M2ThBge6GjiEgtnRUjwWr80wYl9Es4D4RuQvYWzqoqn+VmlTG6GD9eujrg8Xu\nKpfb5nkuhPIsoysv7GDb6TNY7LhctfJ+311dztvePofI6aTrd6yn70Bf4FD6B/qdXeuL/flUcC5D\nt3GXe0GfSkZRQMxpZ3tzx2dGO2EMwqdSl8IYlRTev3vYc9acPmPEPuWd7W3MdMw4P3ZObarqcDPh\n3n27mbo/+F6TDsLjz6o9fvSeSCJnSkOD6I6Y095xcOWFVnoiz4TZhxDOIWsYDsLWvx8JV17YUZG2\nCrB/fIHHpoxsE9tTdy4JfG1VT1dNsPuwA/C5W+Cq50USOzMauomu5FosyzJa9qoi2yygnGvCpJ2+\nGPgKsAAYD7QAe1XVMV8yjMZRWllUu5zaWrc4lf9hB+Bb7+9iTi9snQrLz4ZrTgp3r6Wb22B1keVn\ne++d0+tlRL1kK1wVcxyNyl5q+Ca6qpjTNSd1OV2BecjeyoMMeSCMy+irwEXAdcCpeHsSjk9TKGPs\nEPcP0eVy6uijZibcMugFsOf2es/n9uIsvRFIRwdL77ufpRsqVx9LXxdvXI2uJZS3TXR5qKWUBxny\nQtidyg+KSIuqDgDfEpHfpCyXMQZI6w/RNRP+3E1Flm6oOjGg9Ib7orUuEAoFrjlpHydUKf/2ie3s\n2Lsj1LiaKXspDfIw/jzIkBfCGIRnRGQ8sF5E/g3YDkxKVyxjLJDmH2LYjKTBYpHWCK0dh/BcUkpt\nRtO2vm219wkY11ivJZSH8edBhrwQxiC8BS9u8F7gUuBY4MIkbi4i3wReDexU1ROTuKbRPDTyDzEo\nI2lnexuL5y1yvCMc6x5dF1reoAyfpDN/6pUNSTPIH4ZqV1prodW5Az1o/Gn4+kdrCZORMGzHNFV9\nRFX3qerTqvopVb1MVR9M6P7fBs5L6FpGk9HINolXXtjB/vGVP/f94wux0yCjGC/XuNLqDDa4dknl\nvy9FKBCVEqXVVPlO6aBaVO0T22uOpbVb3LqzDRG4QhCRDfj9TlyoauxkO1X9lYjMjXsdozlpZBpk\nUEZS3Lo6QbPLaoLGlcfy2WlSr5R5OT37emqOpeVinDF5Br37eytcfTMnzRy130E96rmMclG4TkSW\nAcsA5jhq3xjNS6OVYZxNcEEEGbWZk2bSs68n1LjylvmTB1xGNqqLMUqm1469OyqO7di7g6kTpo65\n7yXQIAQVtGs0qroSWAlw6pQp1qltlBFFGeYxV3yszfAbhcu9FsXXHyWDzbKMhrCNaUZTkOdccZvh\nh6cghRrlKwha5p0Ocq9FcTFGUfKWZTTEsEFlvI1pF+M1w5kIXIJnIAyjYdT7AzeaA4GacuMLjljA\nCUecEKoEeZRy5VGUfCOTG/JOphvTROQaYAlwhIg8BnxCVeNWAjBGITaLGx0Eraai9LAOc24U91JD\nazzlnEw3pqnqxUlcxxj9JJErnscYhJEOUZS8xYGGCLsxrUAKG9MMIyxxZ3FpxiByaWiq+hlTCPYO\n51L+mERV8hYH8ghT/voRABEZAH4CPK6qO9MWzDDKiTuLSyuTJI/B7os3UNPPuHS8usl9HuU3sqPe\nxrSvA19R1ftEZCqwDhgADheRD6nqNY0S0jAg3iwurRhEHlMWV6yhpp9x6fjbq/oc5lH+JDBDNzLq\nrRDOVNV3+4/fDmxW1deIyEzgZsAMgtE0RI1BhHWj5DHYPac3/PE8yp8Eo9XQpU29tNMDZY/PAX4E\noKo73KcbRn6JUq8mSs2cPKYsbp0a/nge5U+C0Wro0qaeQdgtIq8WkZOBlwA/AxCRVrz9CIbRNMyY\nPIOZk2ZWHAuqVxNlz0MeC6MtPxtnEHn52bXn5lH+JBithi5t6rmM3gV8GZgJfKBsZXA28NO0BTMy\npDpDpaMjuIlMlHMzJEq9miizyyRSFpPO8vHagg6yYg017UKrW1iO1pRL21swMurVMtqMozS1qv4c\n+HmaQhkZ0t1dm6FyvxeMq1H0Qef29kJPD/1dsLN9Xd2qomf/prumAikkX5U0ik85arwhTrA7jeDn\n4nlL2DavNoDs6mdcuk9Q0bdmNRSj1dCljag2T724U6dM0btPPTVrMUY369YdSlOsoK0NFi0Kd24V\ne8fBsvNrG9pfvMHrczzp4NCxYguowoRBx/ufJ5UXiPDbVfBqJzheqD7sPNe/VelwUo1mghrstLW0\nsejYkTfuiavMu/u62bhrY83x2ZNnV1RxLfYX0bVLKs6Z/qIueie4r5t1g56xStfbuu5R1WGVZ6jS\nFcYYIkjBu46HMAbgKfxVP21j1ZMOg3Kw8hptA+73/8fPYVtVOcXbrm6FM84IJcPcF97BI5Nqm7Ec\n90wrD/+u6hp33MFz3tXPlumegWobgKtWC0une3PswuKuiq5kYZWcS0mnEfxMYtWxuWez83h5z4CS\njKuO6mbpzqHr9o2HqROnsXDmwhHJb2SHGYQsyaP/vbUV+h1drFpba+VtaYEBhwZ3EcOgAMzcC7c9\nXOX0CGcLAFhxq7LsFfDM+KFjhx3wjlOdfXPGGfz5vqpj04ceDpZmxOvXU3j/7lD3D1LSUVtIhiGJ\nlMsBDfm9Cizv2FJhEIzmpd7GtMvqvVFVv5i8OGOIKL76RhLkhhkYqJVXXD6YAFzNjdrawhuFmM2R\nlv5+APoMAgRxAAAgAElEQVS94OrWqV6wdcUaWLphwCuvmDJBSlqQmpLQcYOfjU653NpmqZyjhXor\nhCn+/53AC/HKVgCcD/wqTaHGBFu21O4mHRz0jmdpEIJm/Kq1xkLVWzm0tAytGiZOhN2OWXN7bY9c\nOjoqjQx4Rqb6PoWCd24c2tpYuqHI0g21x+NSr6l9iaAYxsBg7ec9qINsemIjm56o9eGHxnGvKKuO\noJWLiznFtkirJSO/1Msy+hSAiPwCeIGq7vGffxK4riHSjWai+OobSZRZO3jupXI//h13uM/buROm\nTq11kXV21h6D5F1pLuMT19AsXMjg2nCnzn3xOh6ZUPu5Hlds4+Hfjjx47EIWd8VedRx/+PFs2rWp\nonFNdSMbABRWbOnAK4JsQeNmJ0wMYQ6Vu5YPAHNTkWYsEaR4s+4bHaQ4HbVxnLjiD6XjLhdZZ2dt\n9hIkv0oqXS+jmM2KLR0s67yfZ1qGPsfDBgq+Mk0WAQYHa91Tm57YGCqGcMfWOxgY6K9W/agoC45Y\nUBEYL/YX/fjB9sTkN7IjjEH4LnCXiNzgP38N8J30RBojRJ2xbt4M24YyPJg9G+bPD3+/sAHsIMW5\nMYb7okTWLrIZMzJzx5WCrss7trC1rcicYhsrtnSkEowdXLvEWf668OF9nntLhMVzg3YleEzdD0/d\nueTQ85fOXcva4/SQG0uAA/1FZyZvPZp5b8NYIEz56xUicjNwpn/o7ar6+3TFGgNEmbFWGwMYeh7G\nKEQNYLsUZ0nOaqpXNFEyj0qyjBGW7pzRmGwc1/ddKDD4aYGWFgrL+7lj6x2cMSd8mtZtDy+Gh2OK\nZRVIc0/YtNPDgKdV9VsicqSIzFPVh9IUbEwQdsZabQzKj4cxCFED2K7VRHu7W47qYPH8+e7VRL10\n1tIGt7yk3oL7M4B4LqegVVrS6cdB37e/uXDq/i76Eul5GFEsq0Cae4Y1CCLyCeBUvGyjbwHjgKvx\nCt4ZzUCUAHZ3N2zaNJTpUyx6z4PYubPSKM2Y4ZWuqHZvTZ3qzijq7x8yFGmn3oZVvK4ZdvlnUi0r\nDH/d7u5KQ1kses97e2HHjnjpx9XjKhZZdZIrxTbb1ZhVIM0/YVYIrwVOBu4FUNVtIjKl/luMXBEl\ngP3AA+700iCqZ/3d3Z6CK2fHDs8gVGcU9ffXupfSiitEcZtt2cKq5w5WKVStTVkdHPTceaqB9ZwO\njfXAAZy4Vl1RPgPHuK4+Cd51/tAmvEemeaU/npgIly3uAqBl+CvXEpRBFnK3eBJ9sY10CWMQDqiq\nioiXSi2SwWJzjDN7tltxzJ4d7v1RAthBWUJhqeeeWrSoUsl1dbmvkUZcIYLb7Or5RadCBWqNgite\nMjhY+X2NZDxh3+MY1z+dXbkjG7znnzy3lcXzImzvLuOlc9eydrF7YhA29dYqkOafMAbh+yLy38A0\nEXkn8A7gynTFMioouWRGmmWUZsplS9VcM4p7KmjlkkZcIYJcHz3HrVCXn+0wCGkRNv3YIX9Qg5ze\n1pjG3pGdFGZTXok8VCC1LKf6hMky+oKInAM8jRdH+GdVvSV1yYxK5s+PlmZaTdgAdlCWkGsHcUmu\ncuq5p6p93e3tlf7z0n3SiCtEMD6PBzhEaxRtoQCFAqsW9Dv89SHlCvq8w26Yc4xrTq+3qqk5Na5r\nRjWSAXARp1R4XCzLaXjCBJU/r6ofAW5xHDMaRaMK4QVlCZ1wgvf/cDIEuafa22t9+Dt2wMyZlb72\ntOIKQVlSDuMzfR88dVjtqXP6WqCttWL8q+b0suyUbeHcS9WIeGPavr3S2EapEeX4vP/5Nnj3+XCw\n7K+7ZRCKWjyk0FtaWg+lnVbPmg+V0yj7zd1WioNUrwghUpHBtAgz87csp+EJ4zI6B6hW/q9wHDPS\nopGF8IZzLw13v6D3B/nwe3oqdyqnFVfo6Ql33uAgX7nZU+o1lVFvGazZVb385C3h3UsiMH58zeey\n6kStDWBvDmkAHZ/3O55op+3H22pXLf0LYMYMpr9oKO3UNWsGeP52nHsZOP74fKQFlxF25m9ZTsNT\nr9rpe4C/AzpE5I9lL00Bfp22YEYZ9QKipdeTXDkEuZei7HauPh600zmtjWmOVMywlBR5rRtIayqj\nBlX6dPrxVYfkKBbh4YdZNb9YYXwOrTBWF1kaVuDqz3vdOpZucxiktloj45o1Azx4BNH2rixcCAz1\niQjqh5CGDz/szN+ynIan3grhf4Gbgc8CHy07vkdVn0xVKqOSegHRRq0c4q5SGlm7ySVrRJZuCHD5\nlK9gZs9mzsnwiEP5H/4MzP3AMHGFfftYHpARtPxlsLSsHkC9LmSDVR3LogTQg2bH24ISy+t8loMr\nWnnpmwdYe5w7GyktH37Ymb9lOQ1PvWqnvUAvcDGAiBwFTAAmi8hkVd3aGBHHIFEa0TSqPlDcct1h\nU1+DxuryXUeRNSx+IT/3xq6qc7dtY8Wtte6l8f3wdBv0+G6ZenGFoIygrVXd4frGu89zEsH4Bs2a\nZ++pc20X69dTWN7vxz/EuToImslv7tkca9UQduafhyynvBMmqHw+8EVgNrATOA7YCDw37s1F5Dzg\nS3j7ZK5U1c/FvWbT45rduoKM9SqQpuGGiVuuO2zqa1BANei4y40VdfwlBVoKFLdudLtxqFXoLvdS\n37ghY1AiKK4QlBE0p1ipzA7eviT8eCLsO3HNmgGevYva31iIcuH1iuYFzeQHdIABfxIwklVDlJl/\nlllOzUCYoPJngBcDt6rqySLyUvxVQxxEpAX4L7yg9WPA70TkJ6r657jXbmpcs1tXI5pSoLZRbpgk\nXD5hUl/rlc+uJsiNFVQ3KYiqQPHHF26MtA+h2r1U+IT7Nq7VwIo1sOw1heHLYq9fz/R31Tageeo/\nHH2lI+w7cc2aDwwc4A+z1N2rIsbKM2gmX03UzB+b+SdHGINwUFV7RKQgIgVVvU1EPp/AvU8DHlTV\nLQAici1wATC2DULQ7La6EQ3U1gwq4epOFpc0Gsy4iOIyCnJjiYTv4eBYeTwa5MYJOF5N4Ky/t/bY\n0g3Ags5hy2KP+4fdDBRq319Y3u/eKRyh1Hdp1rz2oS4O9Jf9/kZQLrxeUDloNeIiauaPzfyTIYxB\n2C0ik/HaZq4SkZ1AzC2PABwNPFr2/DHgRdUnicgyYBnAnKybxzSCKDPxoFTKsCmWUWhUg5koLqMg\n4zkwAAsW1G6CcxnP0v6KMuY808ojk2p/4i6FzsSJsG9fxaEVa2DZX8Ez44aOHXZQWLHGEWxdsCBU\nWexILqMY1ASow1LWPW7cmV3OU1wz+QEdcLbqtMyfbAhjEC4A9gOXAkuBqcCn0xSqHFVdCawEOHXK\nlDpV1kYJUWbiUauYxlXmjWgwE8VlVM94umR1tfB0jGfFI8ezbP4mnmkd+rkd1i+suGcqUOa2KZUP\nqepXsbRnNjwwtXbW34+X+pm3Ut8NpHomX515BJb5kyVhSlfsBRCRZwGrE7z348CxZc+P8Y+NbaLM\nxMOuJhq5sS0uUVxGKbmxArub7QLa9g19L1N9H5KjrMjSDd0s/TFQBNqADoINatxueGnRgN3x5v/P\nF2GyjN4FfApvlTCI1z1P8X7icfgdcLyIzMMzBBcBb4p5zdFB2Jl4WIUYN2W0kURxGUUxnhGNYo0b\nJ8r7o5wbtxteWjRwEmH+//wQxmX0IeBEVd2V5I1VtV9E3gv8HC/t9Juqel+S9xj1hFWIcVNGG0kU\nlxGEN55BRvGBB8IZlChGNcq94nbDS4tmmkQYiRHGIPwFeCaNm6vqTcBNaVx7zBBGITZyl3BUqt0S\nQSmjcWWtl70VprJqFKMa9V55pJkmEUZihDEIHwN+IyJ34nlEAVDV96UmlZEsjUoZHY7hyl+XlE11\nqe0kZA1bzyhoFhylrHfYfRAj3U3dCPI8iTBSI4xB+G/gl8AGvBiC0Ww0KmW0Hi6fdJC7pFDwlGoY\nWcMGPoPSTl24FOHEie7jhULsuklOwnbDS4u8TCKqsAY36RLGIPSr6mWpS2KkSyNSRku4smZ6esLP\niAcG4Mwzhz8vaqA3LK6Mpt21u4SBmj0IkXHtkUgiyyhuhlAeJhFVWIOb9AljEG7zN4etptJlZBVP\njVrqZc2EJWwLzSiBz6DigC6iNKiJS3t7/G541cTIECos7gJg8SPCbSwObwDWr6fw/gCjmRDW4CZ9\nwhiEUirox8qOJZF2aowGYvQdcBKlhWZagc/+/tpxpUUau8pHaYaQNbhJnzAb0+Y1QhCjCYnad8BV\nPTNOC820Ap8tLenEBVzkrDJtmqUr4mINbtKnXse0s1T1lyLyOtfrqvrD9MQyYhHFfxzH1xy170CY\n6plRWmimEfgsFLxVShQXUxxSWH08PBXmOuouPTwVOnyX0NTxk53vLbmMpu6Hp+5ckrhscbAGN+lT\nb4WwGC+76HzHawqYQcgjae2odRFldjt7dvJ7JqIEPqtTWYOYOTNazGPaNHj66ZGlkJaMV8KlK5af\nDVfdWGDCgSGZ9o8vcPVfd7J4XvDnv3jeEgDu2HoHydSvTBYrc5E+9Tqmlaq6f1pVHyp/zS83YeSR\nJHbUhvU1B9Udqla+URRc1Fl/2OyplpZwewN27Kjfoa6afftqVz71DGVVMx5nCfOYpSuuOQkWHNHJ\nJddv4aieIjvb27jywg7WnN78itPKXKRLmKDy9cALqo79ADgleXGM2CSxozbszD8oG6elpbZ3Q1jS\nSncM2zBncNDLcgrbT6FYrDVK69e701SnTfOb0ZexcaP7ujFLV6w5fcaoMABGY6kXQzgBr03m1Ko4\nwrPweisbeSSKyyVqULY63hC17lBYGrlnwkV/v7eqKZ+5B7mcXJ/VwoW1RmHaNJg1qzad1jByRL0V\nQifwamAalXGEPcA70xTKiEEUl0uUc6NkFDWyvEEaJZpFPNdRmPOClHr1SqBevKaBpLbTtwH7EIz0\nqRdD+DHwYxFZpKrrGiiTEYcoLpco54bNKGpkeYMoQfEoeyRUa1cDrtVBmCB1iaB4TRApbI7r7utm\n464hF1VxoHjoeVJ++VJg2mhOwsQQXisi9wH7gJ8Bzwc+oKpXpyqZUUmUmXAUl0vYc6MEShvl7okS\nFI9SyygKDzyQfEYWOFt7uiiliYZhc8/mwOPlBmHy+Mn0DuyOdG0Whz/VyC9hDMK5qvphEXktXt/j\nNwC3AWYQGkUeOp7VizcsWtQYGaqJEhRPY0cwhI+X1Pv8Ojpiub3CzsoH1J05VX184cyFzvOM0U8Y\ng1BqFf4q4BpVfVIaWevFyEcpgiQ2gSXt748SFI86Qw+bZRSWep9f1kH0UYpVRo1OGIOwWkQ24bmM\n3iMiR+K10zQaRR6alcRNB01jlRPFSEWJIbhm7QcOuGMGrsqoLvJQPVTxGuC6jg/D+h3r6d0XPmic\ndSwhamVUMx4eYWoZfVRE/g3oVdUBEXkGuCB90YxD5KVZSZyZbBqrnChK1mU8XKmkQbP27m73noEs\n21z6hFVm0/fBU4fVvn96iArevft2M7iiNdT+knFndrF+x/pMXU9RKqNaWe0h6u1D+LCq/pv/9GxV\nvQ5AVfeKyHLg440QcFQT1oWS02YlgbjGldYqJ6yRmjGjdlfwrFkwdWryGVkuUooDKYRWZl++Gd5x\nARws+6sf1+8dv+o5IW+YRppvCkSpjGpltYeot0K4CCgZhI8B15W9dh5mEOIRRUHkwd0A4ZRB0LjS\n6pUcJFNQu85yduzwDELYoHjeVkily4RUZi/b1sa3flxk+dmwdSrM6YUVa+DsbW1cFeZGAwPZJzeE\nJKgyKgprH+qqPBQQEh2LZbXrGQQJeOx6bkQlqoLIOvAY1oAFjSsoQDtxYvIy9fbW9mp2pZwmFZgP\nU5yuwXGgkjIrV34fXOwVvVu6obLo3Rfe1lFzrhPX/gzHZzj5APQWdg9/vRQ5JKVUHrz6h7B081Ca\n9FteUWTVQkEdgZSxWFa7nkHQgMeu50ZU8hAojkJYAxZV/qDWlHFkirLfIO7nXa9DXLlRaHAcqFyZ\nDa5dcmgnsavo3f8+D7Y8ug4Fjiu2sWJLB0t3uoxkl/tmVeMKLJu9fv0IRjJyVnUWWf6S/WydoszZ\nI6zoamXpfQMw6MtbLPL1n8Ldc4RNh9eqtPaJ7Q2VNw/UMwjPF5Gn8WzsRP8x/nOrZRSXvASKwxLW\ngCXRNS0sSdwn7ucdZHyqi9OlGAcqSKFuj4DC4q5DG8eqi95VBFQFHplQ5M0LNvLmBRtr3ABbfu/u\ns0BbW03pClejnSxLW2ydorzkLwehav4w6SDsaXWvXnv2pbR3JcfUK10RMp/OGBHNFigOa8CCxpVk\nTn9S1KtFlDRJxYGqYiNvOhx+f1ZnYJbRcOmfroAqwqHrlF/3H9/Wznf/346KPgt7x8G7X1Fk1fOL\nh+639qEuCou7aHF85VHSUZNOBZ3T2+U8vm2K+3yLIRiNIy+B4rCENWBB43LV/QfP354G1UbIlWIa\npRZREsSNAzliJitXwxVHwJrTR7ZbvF42TnX20g8P3wHvmclXru6pcDnd2LGdqQztcF48bwnrd6yn\n2F9kf/9+FEUQTjgiXDkOSCcVdGd7GzN7asd79B547Fm151sMwWgsWQeKoxC1aF5QplSCncEilYM4\ncMB9jc2b430H1WWyy48njSNmMukgXHL9lhH3PgjMxsGdvbT6iB52/Uel8VlI7b1nTZ7F/T33HwrW\nKhpJoaeRCnrlhR186Nv313SSe/HATH4oO6w1J2YQjCiENWBBqaDz5ye7kStKOYigXs1xeyeXxpOk\noQsiIGZylGPWG5agPsU1bqSSCCHdKHEVepR9BGEpGc3qoPquk2fQ2TfVdiqTkUEQkTcAnwQWAKep\n6t1ZyNFUNMmGoIYW4suL2y1pQxdEwIpoZ/vIXRtBfYpLz6tpLbSy7tF1wyrOuAo9aOUS140T1EnO\nWnN6FDK675+A1wG/yuj+zUVJyZaUQUnJdndnK5eLeumpaTBjhrexbMkS7/8gY9AaMPcJOp5HOjq8\nFVAZe8d5rpDEbzW9g4JU3ksQ+gf7Dynqkl+/u6/2dxikuMMqdNf9x6obp5Fk8tegqhsBrGpqSPJQ\n7TQsed1fcfzxsGlTZSBZxDveLDhWRMteVWRbhPhBdeZO+8R2duzdURO87WzvpLO9Mnupf7C/plR2\nkBsoyBUVVqEHrVzSmsVbcTuPJpoejWHyqmRd5HV/RZB7CWr7HLtKX+TFRVcVG7nmpK7QvWlcmTvb\n+moD4iUlv+jYRRVKsevhLud1Xa6dJBR6FDdOHIVuxe2GSM0giMitwEzHS8v99pxhr7MMWAYwJ2ul\nkhVpKtkoii/MuR0d7pl4HvZXuCqYhi19kdOaPUG4FKRzz0EAQf77KH79Rvnl4yp0K243RGoGQVVf\nltB1VgIrAU6dMmVslsxIaxNblABwPeXZ01NZRK6R+f5xZvJRSl/k1UXnIEhBhjUGQcR1A6VFHjOa\nmhVzGTUDaWXTRIlNhFGeQUXkSu9PWpnGzWiK6nKL66JrkBsqSEHGpdF+/bDkNaOpGckq7fS1wFeA\nI4Gfish6VX15FrI0DWlsYosSm4irDNOId8QNtketuxTHRdfAdNwkZrYt4q5ck8f0zCgK3eVKy+vK\nJwuyyjK6Abghi3sbZUSJTcQtWtfWlvwMOW6wPcgVN3NmZQyhdDyOi66BmWL1dh9HoVkyb8Iq9CBX\nmiujKq9jTRtzGY1GonRiCxsAdinPsBQKXmwh6RlyUNOdoL0Frs+ls9P9WYXtpBaWBmaKtU9sd2YP\nRWFAB5om8yasK6terKE6o2qsYgZhtBHVNRE2AOyKY5S6kLlm2OWB5lJdoaRnyEGyuo4HfS6dne6O\naUm76FLMFKueyVfvFRgpjcq8adRKxILHw2MGIY+kkTkTFCgOukbYonVhZ9KuBvUQb4YcVIfIdTzr\nzX0pZYq5eiqnSdLXT2IPQNhrWPB4eMwg5I20MmeiBIqjKOmwM+k0ZshRrpn15r4U6y6FzSBqkRYG\nddDZLtJ1rmulkbTyTGIPQNhrWPB4eMwg5I2gmezmzeGUSRKB4jQ2AKYxQ262JkMZljsXBBFBHe60\nFmmhtdBa4bIBQivPOC6fJNw49a5RXYjPgsf1MYOQN4JmrAMDQ66QequGKEqyvd29b6A9hV6yacyQ\n81LttAlQlP5BRwAeL4B85rFnOl8bTnnGdfkk4capl1VVXYivs72TRcc6YkYGYAYhf4RN7wzyf0dR\nkj0BPWO7u2uDwkko2TRmyM3UZKjJCLPnIK7LJwk3jusaLsZqOYoomEHIG65U0CCCDEdYJRl3NWLk\nAkEq4wIKuAoJBx2PQb2ZeRhXUlJF8KqvYRlFI8MMQh4JW/snrq8/7mqkmchrFdYEqAkSByj9FoUB\nx2tR3DPVSj4o+NxaaA3tSkpi93P1NUqxg2oso6g+WTXIMYII20gmieBpR4e3ES0MeSy1HQVHc5lc\nB6DjUmUjDjsASwJ+Wu0Tw8WMSvGCcr+8y01TkAKqGuhKagTWYGdk2Aohb9RTvKVZbhJlqks0ajUS\nhSD54+zPGGMB6PZnYPJB2DoV5vTCijXwkXPc5/bsC4glVeGKFyhKa6GVFmmpcPls3OXed9Iol02Q\nK+qBJx/ggScfqDj3jDlnNESmZsAMQjPh2lFbTZR9DFFXIy6FXLrOSJVs9TWrdz8n2aNglAagC1Ko\nUNQtg/Cln8HSDZXnvfl17veHVdJB5/UP9nPG3EqlGtSTuZEum2o30tqHumgZhMkHhs7pnQDrd6xn\n4cyFDZMrz5hBaHaqFWp/f/gduVFWI1C527hYrN19HFVJu4xXUC+CJu9RkBYCNbn1RS2ytH8BtJX9\nLgoFYJ/zGmGVdJQU0bxuAjv42VY4Y8h4jTuzKzthcogZhLwRJfjpUqhBBF0z6Hj1auT224OvXU4U\nJe3ahBeVZo9tJIBrJlyzGlq/HthXs5qIoqSjKPm89k4w6mMGIW9E2VgWRaG6DEqUewXVDXIRVkkn\nocxHQZZQo3CtJqIo6ahKPo+9E4z6mEHIG1GCn2EVapCSTyvQmrSSTqtHwRgkrpI2JT+6MYOQR+IW\njGtthZaWcEo+6UBrUkralVGVdI8CwzAqMIPQzAS5fI4/vrGKMmw6bND7XMcb0aPAMIwKzCA0M43M\nrZ89253pM3s2zJ8/sms2W7VSwxjlmEFodho1ay4p/XKjEMcYwJjbLGYYeccMghGe+fPjGQAX5gYy\njNxgtYwMwzAMwAyCYRiG4WMGwTAMwwDMIBiGYRg+ZhAMwzAMwAyCYRiG4WMGwTAMwwAyMggi8u8i\nsklE/igiN4jItCzkMAzDMIbIaoVwC3Ciqj4P2Ax8LCM5DMMwDJ9MDIKq/kJV+/2nvwWOyUIOwzAM\nY4g8xBDeAdwc9KKILBORu0Xk7icOHmygWIZhGGOL1GoZicitwEzHS8tV9cf+OcuBfmBV0HVUdSWw\nEuDUKVM0BVENwzAMUjQIqvqyeq+LyNuAVwNnq6opesMwjIzJpNqpiJwHfBhYrKrPZCGDYRiGUUlW\nMYSvAlOAW0RkvYh8PSM5DMMwDJ9MVgiq+uws7msYhmEEk4csI8MwDCMHmEEwDMMwADMIhmEYho8Z\nBMMwDAMwg2AYhmH4mEEwDMMwADMIhmEYho8ZBMMwDAMwg2AYhmH4mEEwDMMwADMIhmEYho8ZBMMw\nDAMwg2AYhmH4mEEwDMMwADMIhmEYho8ZBMMwxiyTD2QtQb6QZmpnLCJ7gPuzliMFjgB2ZS1ECozW\nccHoHdtoHReM3rGFGddxqnrkcBfKpGNaDO5X1VOzFiJpRORuG1dzMVrHNlrHBaN3bEmOy1xGhmEY\nBmAGwTAMw/BpNoOwMmsBUsLG1XyM1rGN1nHB6B1bYuNqqqCyYRiGkR7NtkIwDMMwUsIMgmEYhgE0\nmUEQkX8RkT+KyHoR+YWIzM5apqQQkX8XkU3++G4QkWlZy5QEIvIGEblPRAZFpOlT/kTkPBG5X0Qe\nFJGPZi1PUojIN0Vkp4j8KWtZkkREjhWR20Tkz/7v8P1Zy5QUIjJBRO4SkT/4Y/tU7Gs2UwxBRJ6l\nqk/7j98HPEdV352xWIkgIucCv1TVfhH5PICqfiRjsWIjIguAQeC/gQ+p6t0ZizRiRKQF2AycAzwG\n/A64WFX/nKlgCSAi/xfoA76jqidmLU9SiMgsYJaq3isiU4B7gNeMku9MgEmq2ici44A7gPer6m9H\nes2mWiGUjIHPJKB5rNkwqOovVLXff/pb4Jgs5UkKVd2oqqNld/lpwIOqukVVDwDXAhdkLFMiqOqv\ngCezliNpVHW7qt7rP94DbASOzlaqZFCPPv/pOP9fLJ3YVAYBQERWiMijwFLgn7OWJyXeAdyctRBG\nDUcDj5Y9f4xRolzGAiIyFzgZuDNbSZJDRFpEZD2wE7hFVWONLXcGQURuFZE/Of5dAKCqy1X1WGAV\n8N5spY3GcGPzz1kO9OONrykIMy7DyBIRmQxcD3ygytPQ1KjqgKouxPMonCYisdx9uatlpKovC3nq\nKuAm4BMpipMow41NRN4GvBo4W5souBPhO2t2HgeOLXt+jH/MyDG+f/16YJWq/jBredJAVXeLyG3A\necCIEwNyt0Koh4gcX/b0AmBTVrIkjYicB3wY+CtVfSZreQwnvwOOF5F5IjIeuAj4ScYyGXXwA69X\nARtV9YtZy5MkInJkKRtRRCbiJTvE0onNlmV0PdCJl7XyCPBuVR0VMzQReRBoA3r8Q78dDRlUIvJa\n4CvAkcBuYL2qvjxbqUaOiLwS+E+gBfimqq7IWKREEJFrgCV4pZS7gU+o6lWZCpUAInIGcDuwAU9v\nAHxcVW/KTqpkEJHnAf+D91ssAN9X1U/HumYzGQTDMAwjPZrKZWQYhmGkhxkEwzAMAzCDYBiGYfiY\nQTAMwzAAMwiGYRiGjxkEwwiJiLxGRFRETshaFsNIAzMIhhGei/EqSl6ctSCGkQZmEAwjBH4tnDOA\nv4Q1nyMAAAFOSURBVMXboYyIFETka34t+htF5CYReb3/2ikislZE7hGRn/tlmA0j15hBMIxwXAD8\nTFU3Az0icgrwOmAucBJwCbAIDtXO+QrwelU9BfgmMCp2NBujm9wVtzOMnHIx8CX/8bX+81bgOlUd\nBHb4xcXAK69yInCLV0qHFmB7Y8U1jOiYQTCMYRCRw4GzgJNERPEUvAI3BL0FuE9VFzVIRMNIBHMZ\nGcbwvB74rqoep6pz/X4cD+F1GLvQjyXMwCsOB3A/cKSIHHIhichzsxDcMKJgBsEwhudialcD1wMz\n8bqm/Qn4Ol4nrl6/vebrgc+LyB+A9cDpjRPXMEaGVTs1jBiIyGS/yXk7cBfwElXdkbVchjESLIZg\nGPG40W9SMh74FzMGRjNjKwTDMAwDsBiCYRiG4WMGwTAMwwDMIBiGYRg+ZhAMwzAMwAyCYRiG4fP/\nAfyzKuSV3NT5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14150b50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14717ff0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Visualising the Training set results\n",
"X_set, y_set = X_train, y_train\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Random Forest Classifier (Training set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"# Visualising the Test set results\n",
"X_set, y_set = X_test, y_test\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Random Forest Classifier (Test set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}