diff --git a/machine_learning/Random Forest Classification/Random Forest Classifier.ipynb b/machine_learning/Random Forest Classification/Random Forest Classifier.ipynb
new file mode 100644
index 000000000..7ee66124c
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+++ b/machine_learning/Random Forest Classification/Random Forest Classifier.ipynb	
@@ -0,0 +1,196 @@
+{
+ "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": 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UfCOTG/JOphvTROQaYAlwhIg8BnxCVeNWAjBGITaLGx0Eraai9LAOc24U91JD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\nAfyzKuSV3NT5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x14150b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ewFBMCyUF4gSEH5jZN4DpZvYu4B3ApekmqwlUaiStpf46r4Embu8pSGdsQRVt\nKw9Oiz7FxqjtNY6gnrs9KIFEbc+diN5JjdrjSKLF6WX0BTM7GXiSYNTyJ9z9htRT1gyiqmtqnaIh\nrd44tQaaqM+Xk8bYgipGOh/0JDwUkflHZtLlzhtO8Dfab7B8ZStLXzO8RPKM3cF2ygSmzLgrAIxz\nowYEM/u8u38EuCFimyQtiSka0uiNU2ugifp8Nb2UalVFldfnfg3vPpWRmfSNQGnbeLnzxpzgb8mu\nw+GaNSw7ad9gqeU3wpKBw2sKCKNl3K2tbRW7nZa6+f5FcEW5NoQqEia5FqfK6GSCXkbFXhOxTZKQ\nxykaCmoNNFGfnzYtd2ML3rK+Hbumf2Qmvb4dStvP41aFlQvqHR0s6YUl/53cdxA5MK3IjGPidTsd\noUEaj2XsKs12+l7gH4BOM7uzaNdU4HdpJ6xpNdsUDfUcWxBXZydL7l7HkrtKSi7zI0ou1VSF1drT\nK88WLAD2rRMxbfJ0FsxekGmSpHqVSgj/A1wPfA74aNH2He7+eKqpamaaoiEd1QTaaqrHoo4dHIxe\nK6KGoD7jmC62T4reN1qJoF6GlrfxircMsvJQrZ7cqCrNdrod2A6cBWBms4BJwBQzm+LuG+uTxCaj\nKRrSUW2greapvfTY0o4Bo10rRq+yvonRH82N7m5alg2EExaaSgcNKk6j8qnAF4EDgS3AocAa4Pm1\nXtzMTgG+RDBO5lJ3/9dazzkujIcqhLypZ6Ct5loxe5Xt+e3i5NOZgtEmzZN8i9Oo/BngWODX7n6U\nmb2CsNRQCzNrBb5K0Gj9EHCbmf3M3e+p9dwNo47z5QixA+0r5q2ku2N4tceCXgt62iR8rdi9yrq7\nmfHukct1PvEfKa0rLU0pTkDY4+5bzazFzFrc/WYz+3wC134ZcJ+79wCY2ZXAaUBzBAQtCZlbUXXg\nKw91uD+Fi8XsVTbhn7YxGDHzWMuygcRGCicxxkCNyo0tTkDYZmZTCJbNXGFmW4DaxtUHDgIeLHr/\nEHBM6UFmthRYCjB3PPW0yfGSkM2uro20MRu761VlNOZ7L1o9bsIJXUklR+ps1NlOCZ7adwLnAb8A\n/o9gXeW6cPdL3P1odz/6gAkT6nXZ9OV5vIHUT2dn0OBcTL3KJCNxpq54CsDM9gOuSfDaDwOHFL0/\nONzWHJptvIFEU68yyZE46yG828w2A3cCtwN/DP+u1W3AYWb2bDObCJwJ/CyB8zYGPRmKSM7EaUP4\nEHCkuz9738tOAAAQ1UlEQVSW5IXdfcDM3gf8kqDb6WXufneS18i1NJ8Mo3ovpXUtqY06F0iOxAkI\n/wc8ncbF3f064Lo0zt0Q0hhvEJXBrFkTDBhy37dNmU5l9eoSrM4FkiNxAsIFwO/N7BZgb6W3u78/\ntVTJ2EVlMLAvGBQo0ymvnk/t6lwgORInIHwDuAm4C4gxg5dkqpqMRJlOtCSe2uNW26lzQSJ6+3q1\nBGYC4gSEAXf/YOopkWRUszKZMp1otT61V1NtN3t2/daEGKd6+3pZt3UdQx58h/2D/azbGpToFBSq\nEycg3BwODruG4VVGmvE0j8pNx1ycGcG+TKfWuvL16+GRR/a9P/BAOPzw2u4ha7U+tVdTbbd1K8yf\nn5sG/5ZFXQAseqDKqTq6u2k5d+TUGvXQ80TP3mBQMORD9DzRo4BQpTgB4e/Cvy8o2uaAHmHyqFzv\npXLbaqkrLw0GsO99HoNC3OBX6xTk1VbbaTLDmvQPRn/f5bZLeXEGpj27HgmRBJXLYEq3rV5dW115\naTAo3p63gFBNQ3GtXYIbuNquEaeuaG9tj8z821vz9d02gkorpp3o7jeZ2Rui9rv7j9NLltRFmj1c\nVq/ORRXIXvXs3llttV3CCtU+lUybOKXiZ6ftgiduWZxcolLUOaNzWBsCQIu10DlDlRjVqlRCWETQ\nuyhq3iIHFBAaXWvrvoXgS7fXqhBU8jLmoZrgV2u302qq7VL6ThY9e/GYP7Nq4yqSmb+yPgrtBOpl\nVLtKK6ZdFL78tLtvKN5nZqpGGg/Mqtte6sADy1cbFcvDmIdqGoqTKE3ErbaTRHRM6VAASECc2U6v\nitj2o6QTIhmIWve30vZShx8eBIU4sh7zUM3cURosJk2qUhvCcwmWyZxW0o6wH8HaylKrrFdMS2JQ\n1OGHD29ALrQd1HLONORhVtGsf2+RUVRqQ5gPvA6YzvB2hB3Au9JMVFPIw6RmtXavrNc5k5Jl987e\nXli7dvjAtLVr96Wr0WU4DkGSU6kN4afAT83sOHdfXcc0NYc8TGqWxlNzHp7Ey4n7hF6u5NTWNvbe\nU/feO3JgmnuwPQ/fTULG0pgt+RFnYNrpZnY3wappvwBeBHzA3a9INWXjXV7qqdN4aq7mnPWqRqmm\nRBZVyjEL2lYK7SvVluhqba8pI04X02pNmTiF7YPbqjt3FYOaJb/iBIRXufv5ZnY6wbrHbwJuBhQQ\nalHvSc3yWH9dz2qzakpkUaWcgYGRXXTz0HuK5J/KF8xekOj5pHHECQiFhYz/Gvi+uz9ucbslSnn1\nrGvPQ3tFlHpWm1VbIist5XR1Vff50gBcOiitIIkxHyIJiRMQrjGztQRVRu81swOAXekmqwnUs649\nD+0VUepZbVbrILxqSnRRAbjcQ1Tepvgoo3tzN9t3xm80VltCY4ozl9FHzezfgO3uPmhmTwOnpZ+0\nJlCvXi95aa8oVc9qs1oH4VVToosKwO5Bo3Rra76q7WLavnMbQ8vbYOHCUY+dcEIX3Zu7VfXUgMoO\nTDOz84venuTugwDu/hSg1dIaSbkMNuuxAdUMFqtVrY26HR3BNNWF76y9PXgflaGXC7QDA3DccbB4\ncfB3gwQDaR6VSghnAv8Wvr4A+GHRvlOAj6WVKElYXscGpFltVlqHX67KqJqgGLdEl+NV0E76fS/n\nXNXDrK39bJnZzqVndHLj8c0RmFZu6IrcPm3ydJVmQpUCgpV5HfVe8izPYwPSqDaLW4efVlDMUQAu\nzgTPugs+dG0Lk3YH6Zq9tZ8PXR50Lrjx+I6yGWa1puyG7S3bEjtfkkqrvQpTdWsJzkClgOBlXke9\nl7xrpkVYsq7Dz0EALmRwDhza387ym1t4+dqde4NBwaTdQ5xzVc/eUkLF9RBGbz4AKkyb3d0d7wRp\nWjiyJNA/0K8lOEOVAsKLzOxJgtLA5PA14XvNZST5VakOP0ajaCIyDMDD1hg2eGBSP285Bb73NMy7\na+Txs7bu+77GMtBtaOXiEVNXRAWWPExtMbR81Yh/A7sGduElz7jNugRnpakr1EFaGlOO6/DrIWqN\nYQwuOBneEhEQtswMvpexdBVduaGLCSd0MRiOVF707MWs3NBFy6IuWiOWlc6yO+rKDV20LBugdahr\n77bBFkYEg4JmXIIzzjgEkcaSozr8LJTLyB6aCrsmtgyrNto1sYVLzxj797Lo2Yvp3hxUBRUaZou3\nFat3w21pu8BzDziCTX2bRhy3c89OLcEZUkCQ8ScHdfhZKrvGcFs7Xzi7M/FeRlEZfda9doZVm7Gv\nXWD+zPkjqoFKj4XmXYJTAUHGp2ZqRC9RaY3hGw/paIpuplHVZuXaBbQE5z6ZBAQzexPwSeAI4GXu\nfnsW6RAZj5LK4Bq5K2a5arNy27UEZyCrEsJfgDcA38jo+jIWeZwxVSLVmsGVq3IpnDvvylabNWG7\nQDUyCQjuvgZAs6Y2kHrOmKrAk7lqqlyqUa9SR6VqMylPbQgyUlSGXK8ZU/M6Vfc4FpVJV1vlEvc6\n9Sp1qF1gbFILCGb2a2B2xK5l4fKccc+zFFgKMLdJ+pFnqlyGXBoMCpKeMTWvU3XnWC1P3eUy6VZr\nZdBHzv1US5VLWqWOctQuUL3UAoK7vzKh81wCXAJw9NSpmjIjbeUy5HKSDtJ5nao7pxxqeuoul0m3\ntbTRQkuiVS5plDokWWWnv5YmVSnjLW3zMUt+sFdep+rOsXJP3XGUy4wHhgaYP3P+3hJBe2t7ZB/+\napQrXaihNz+y6nZ6OvBl4ADg52bW7e6vziItUqLctA9tbSPXDohaErJWTT7KOClxn7or9cZJuspF\nDb35l1Uvo6uBq7O4dtOK23OnXIZcLvNPum6/yUcZJyXuU3elTDrpHkFq6M0/9TJqBtX03CmXIa9Z\nE33uNOr2m3iU8Vi02Njr+stl0lBb20Sl6ykA5JcCQh4l3Q+/2p47URlyIT2lVLefKQPmz5xf01N3\nVCa9+sHVde0RJPmggJA3afTDT6LnTqPV7TfR4LY0nrrVI6g5qZdR3lR6mh+rJHruVLPIfNYKQbUQ\n8ApBtbc323Q1EPUIak4qIeRNGv3wk3q6b5S6fQ1uq5l6BDUnBYS8SWO1r2bruaPBbTVTj6DmpICQ\nN2nV1TfK030SKo2lWL163AXFtCaMU4+g5qOAkDfN9jSfhqigahYMrCsMrhsnk+aVm7ri/m330942\nvFSZ9Spmkn8KCHmUxtN8Wr1u8tibJyqoDgzAYMlkbeOkXSGqe+jOPTvZ079z2PaVG7oyXeRe8k8B\noRmkNaV0nqeqLg2qXV3Rx43jdoVB9SGUKikgNIO0et3UuzdPHksj0lBWbVw1YtvCuQszSEk+KSA0\ng7R63dSzN0+eSyMZK526Aocr1hzBki1F30t3Ny3nbqt/4nJk5YYuWodgyu5927ZPgu7N3WpfCalQ\n2QzSmlK6nlNV1zpgb5xOq12YuqJ4mmpgeDCQvfZ8ro0nblm8909rhaU+mpFKCM0gra6saZ03qmqo\n1tJIo029UYXS7qErN3RllxhpaAoIzSCtrqxpnLdc1VDUegwQ/wlf3XlFRqWA0CzSGpiW9HnLVQ2Z\nBU/0tTzhN9PgPJExUBuC5Eu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+      "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
+}
diff --git a/machine_learning/Random Forest Classification/Social_Network_Ads.csv b/machine_learning/Random Forest Classification/Social_Network_Ads.csv
new file mode 100644
index 000000000..4a53849c2
--- /dev/null
+++ b/machine_learning/Random Forest Classification/Social_Network_Ads.csv	
@@ -0,0 +1,401 @@
+User ID,Gender,Age,EstimatedSalary,Purchased
+15624510,Male,19,19000,0
+15810944,Male,35,20000,0
+15668575,Female,26,43000,0
+15603246,Female,27,57000,0
+15804002,Male,19,76000,0
+15728773,Male,27,58000,0
+15598044,Female,27,84000,0
+15694829,Female,32,150000,1
+15600575,Male,25,33000,0
+15727311,Female,35,65000,0
+15570769,Female,26,80000,0
+15606274,Female,26,52000,0
+15746139,Male,20,86000,0
+15704987,Male,32,18000,0
+15628972,Male,18,82000,0
+15697686,Male,29,80000,0
+15733883,Male,47,25000,1
+15617482,Male,45,26000,1
+15704583,Male,46,28000,1
+15621083,Female,48,29000,1
+15649487,Male,45,22000,1
+15736760,Female,47,49000,1
+15714658,Male,48,41000,1
+15599081,Female,45,22000,1
+15705113,Male,46,23000,1
+15631159,Male,47,20000,1
+15792818,Male,49,28000,1
+15633531,Female,47,30000,1
+15744529,Male,29,43000,0
+15669656,Male,31,18000,0
+15581198,Male,31,74000,0
+15729054,Female,27,137000,1
+15573452,Female,21,16000,0
+15776733,Female,28,44000,0
+15724858,Male,27,90000,0
+15713144,Male,35,27000,0
+15690188,Female,33,28000,0
+15689425,Male,30,49000,0
+15671766,Female,26,72000,0
+15782806,Female,27,31000,0
+15764419,Female,27,17000,0
+15591915,Female,33,51000,0
+15772798,Male,35,108000,0
+15792008,Male,30,15000,0
+15715541,Female,28,84000,0
+15639277,Male,23,20000,0
+15798850,Male,25,79000,0
+15776348,Female,27,54000,0
+15727696,Male,30,135000,1
+15793813,Female,31,89000,0
+15694395,Female,24,32000,0
+15764195,Female,18,44000,0
+15744919,Female,29,83000,0
+15671655,Female,35,23000,0
+15654901,Female,27,58000,0
+15649136,Female,24,55000,0
+15775562,Female,23,48000,0
+15807481,Male,28,79000,0
+15642885,Male,22,18000,0
+15789109,Female,32,117000,0
+15814004,Male,27,20000,0
+15673619,Male,25,87000,0
+15595135,Female,23,66000,0
+15583681,Male,32,120000,1
+15605000,Female,59,83000,0
+15718071,Male,24,58000,0
+15679760,Male,24,19000,0
+15654574,Female,23,82000,0
+15577178,Female,22,63000,0
+15595324,Female,31,68000,0
+15756932,Male,25,80000,0
+15726358,Female,24,27000,0
+15595228,Female,20,23000,0
+15782530,Female,33,113000,0
+15592877,Male,32,18000,0
+15651983,Male,34,112000,1
+15746737,Male,18,52000,0
+15774179,Female,22,27000,0
+15667265,Female,28,87000,0
+15655123,Female,26,17000,0
+15595917,Male,30,80000,0
+15668385,Male,39,42000,0
+15709476,Male,20,49000,0
+15711218,Male,35,88000,0
+15798659,Female,30,62000,0
+15663939,Female,31,118000,1
+15694946,Male,24,55000,0
+15631912,Female,28,85000,0
+15768816,Male,26,81000,0
+15682268,Male,35,50000,0
+15684801,Male,22,81000,0
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\ No newline at end of file
diff --git a/machine_learning/Random Forest Classification/random_forest_classification.py b/machine_learning/Random Forest Classification/random_forest_classification.py
new file mode 100644
index 000000000..d5dde4b13
--- /dev/null
+++ b/machine_learning/Random Forest Classification/random_forest_classification.py	
@@ -0,0 +1,69 @@
+# Random Forest Classification
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Social_Network_Ads.csv')
+X = dataset.iloc[:, [2, 3]].values
+y = dataset.iloc[:, 4].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.cross_validation import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
+
+# Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Fitting Random Forest Classification to the Training set
+from sklearn.ensemble import RandomForestClassifier
+classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
+classifier.fit(X_train, y_train)
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+# Visualising the Training set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_train, y_train
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Random Forest Classification (Training set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
+
+# Visualising the Test set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_test, y_test
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Random Forest Classification (Test set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
\ No newline at end of file
diff --git a/machine_learning/Random Forest Regression/Position_Salaries.csv b/machine_learning/Random Forest Regression/Position_Salaries.csv
new file mode 100644
index 000000000..0c752c72a
--- /dev/null
+++ b/machine_learning/Random Forest Regression/Position_Salaries.csv	
@@ -0,0 +1,11 @@
+Position,Level,Salary
+Business Analyst,1,45000
+Junior Consultant,2,50000
+Senior Consultant,3,60000
+Manager,4,80000
+Country Manager,5,110000
+Region Manager,6,150000
+Partner,7,200000
+Senior Partner,8,300000
+C-level,9,500000
+CEO,10,1000000
\ No newline at end of file
diff --git a/machine_learning/Random Forest Regression/Random Forest Regression.ipynb b/machine_learning/Random Forest Regression/Random Forest Regression.ipynb
new file mode 100644
index 000000000..17f4d42bf
--- /dev/null
+++ b/machine_learning/Random Forest Regression/Random Forest Regression.ipynb	
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Importing the libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.ensemble import RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Importing the dataset\n",
+    "dataset = pd.read_csv('Position_Salaries.csv')\n",
+    "X = dataset.iloc[:, 1:2].values\n",
+    "y = dataset.iloc[:, 2].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
+       "           max_features='auto', max_leaf_nodes=None,\n",
+       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
+       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+       "           n_estimators=300, n_jobs=1, oob_score=False, random_state=0,\n",
+       "           verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fitting Random Forest Regression to the dataset\n",
+    "regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)\n",
+    "regressor.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Predicting a new result\n",
+    "y_pred = regressor.predict(6.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 160333.33333333]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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94bYm67wwK63TUR1mZluMDRuchDoUEY+SdTB4Ang6xTAN+DZwRupgsBNwdVrlamCnVH4G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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa760ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualising the Random Forest Regression results (higher resolution)\n",
+    "X_grid = np.arange(min(X), max(X), 0.01)\n",
+    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
+    "plt.scatter(X, y, color = 'red')\n",
+    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
+    "plt.title('Truth or Bluff (Random Forest Regression)')\n",
+    "plt.xlabel('Position level')\n",
+    "plt.ylabel('Salary')\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
+}
diff --git a/machine_learning/Random Forest Regression/random_forest_regression.py b/machine_learning/Random Forest Regression/random_forest_regression.py
new file mode 100644
index 000000000..fce58b1fe
--- /dev/null
+++ b/machine_learning/Random Forest Regression/random_forest_regression.py	
@@ -0,0 +1,41 @@
+# Random Forest Regression
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Position_Salaries.csv')
+X = dataset.iloc[:, 1:2].values
+y = dataset.iloc[:, 2].values
+
+# Splitting the dataset into the Training set and Test set
+"""from sklearn.cross_validation import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"""
+
+# Feature Scaling
+"""from sklearn.preprocessing import StandardScaler
+sc_X = StandardScaler()
+X_train = sc_X.fit_transform(X_train)
+X_test = sc_X.transform(X_test)
+sc_y = StandardScaler()
+y_train = sc_y.fit_transform(y_train)"""
+
+# Fitting Random Forest Regression to the dataset
+from sklearn.ensemble import RandomForestRegressor
+regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)
+regressor.fit(X, y)
+
+# Predicting a new result
+y_pred = regressor.predict(6.5)
+
+# Visualising the Random Forest Regression results (higher resolution)
+X_grid = np.arange(min(X), max(X), 0.01)
+X_grid = X_grid.reshape((len(X_grid), 1))
+plt.scatter(X, y, color = 'red')
+plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
+plt.title('Truth or Bluff (Random Forest Regression)')
+plt.xlabel('Position level')
+plt.ylabel('Salary')
+plt.show()
\ No newline at end of file