diff --git a/Machine_Learning_Projects/iris  SVM.ipynb b/Machine_Learning_Projects/iris  SVM.ipynb
new file mode 100644
index 0000000..45760ff
--- /dev/null
+++ b/Machine_Learning_Projects/iris  SVM.ipynb	
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as p\n",
+    "from sklearn import datasets,metrics,svm,cross_validation\n",
+    "import numpy as n\n",
+    "import matplotlib.pyplot as pl\n",
+    "from matplotlib.colors import ListedColormap\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "iris1 = datasets.load_iris()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "irisd = iris1.data[:,:2]\n",
+    "irist = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#x_train,x_test,y_train,y_test = cross_validation.train_test_split(iris.data,iris.target,test_size = 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "e = svm.SVC(kernel = 'rbf',C = 1000000, gamma = 0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1000000, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma=0.1, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e.fit(irisd,irist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x_min = irisd[:,0].min() - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_min = irisd[:,1].min() - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x_max = irisd[:,0].max() + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_max = irisd[:,1].max() + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = (x_max - x_min)/100\n",
+    "xrange = n.arange(x_min,x_max,h)\n",
+    "h = (y_max - y_min)/100\n",
+    "yrange = n.arange(y_min,y_max,h)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.056000000000000008"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.044000000000000004"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100,)"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xrange.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100,)"
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yrange.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "xx,yy = n.meshgrid(xrange,yrange)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.   ,  1.   ,  1.   , ...,  5.356,  5.356,  5.356])"
+      ]
+     },
+     "execution_count": 276,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xx.ravel()\n",
+    "yy.ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = n.c_[xx.ravel(),yy.ravel()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = e.predict(q)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pred = pred.reshape(xx.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pl.figure()\n",
+    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
+    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x263ec8f86a0>"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x263ec8f86a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pl.figure()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x263ec969c50>"
+      ]
+     },
+     "execution_count": 283,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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SU6dCSUn2+K5dIthz9//Nb+Smbu+l03QbWzQ26WwKFz+dFONipTJ/Q5TQQSmtjKAhVYjt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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x263ec880198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#l.contour(xx,yy,pred)\n",
+    "pl.pcolormesh(xx, yy, pred, cmap=cmap_light, shading = 'gouraud',edgecolor='face') \n",
+    "pl.scatter(irisd[:,0],irisd[:,1],c = irist,cmap=cmap_bold)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
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