12-SUPPORT VECTOR MACHINES.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about support machine learning.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25d35883ba8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(X[:,0],X[:,1], c=y, s=50, cmap='autumn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=SVC(kernel='linear', C=1E10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10000000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=make_circles(100, factor=.1,noise=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=SVC(kernel='linear').fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=SVC(kernel='rbf',C=1E6,gamma='auto').fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=make_blobs(n_samples=100, centers=2, random_state=0, cluster_std=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25d36c75278>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(X[:,0],X[:,1], c=y, s=50, cmap='autumn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "kanser=load_breast_cancer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(kanser.data,kanser.target,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc=SVC(gamma='auto').fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.6293706293706294\n"
     ]
    }
   ],
   "source": [
    "print(svc.score(X_train,y_train))\n",
    "print(svc.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_on_training=X_train.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_on_training=(X_train-min_on_training).max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled=(X_train-min_on_training)/range_on_training\n",
    "X_test_scaled=(X_test-min_on_training)/range_on_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc=SVC(gamma='auto').fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9483568075117371\n",
      "0.951048951048951\n"
     ]
    }
   ],
   "source": [
    "print(svc.score(X_train_scaled,y_train))\n",
    "print(svc.score(X_test_scaled,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9882629107981221\n",
      "0.972027972027972\n"
     ]
    }
   ],
   "source": [
    "svc=SVC(C=1000,gamma='auto').fit(X_train_scaled,y_train)\n",
    "print(svc.score(X_train_scaled,y_train))\n",
    "print(svc.score(X_test_scaled,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
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