Support Vector Machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "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.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Data.xlsx')\n",
    "df = df.fillna(0)\n",
    "X = df.iloc[:, [4]].values\n",
    "y = df.iloc[:, [0]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['M'],\n",
       "       ['M'],\n",
       "       ['F'],\n",
       "       ['M'],\n",
       "       ['F'],\n",
       "       ['F'],\n",
       "       ['F'],\n",
       "       ['M'],\n",
       "       ['M'],\n",
       "       ['M']], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['France'],\n",
       "       ['Spain'],\n",
       "       ['Germany'],\n",
       "       ['Spain'],\n",
       "       ['Germany'],\n",
       "       ['France'],\n",
       "       ['Spain'],\n",
       "       ['France'],\n",
       "       ['Germany'],\n",
       "       ['France']], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder = LabelEncoder()\n",
    "X[: , 0] = labelencoder.fit_transform(X[ : , 0])\n",
    "onehotencoder = OneHotEncoder(categories='auto')\n",
    "X = onehotencoder.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zawar.LAPTOP-L2O17FMQ\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:251: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "labelencoder = LabelEncoder()\n",
    "y[: , 0] = labelencoder.fit_transform(y[ : , :])\n",
    "onehotencoder = OneHotEncoder(categories='auto')\n",
    "y = onehotencoder.fit_transform(y).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Data By Taking Z-scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[:,0]\n",
    "X_train = X_train.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.63245553 -0.63245553  1.58113883 -0.63245553 -0.63245553 -0.63245553\n",
      "   1.58113883]]\n",
      "shape: (1, 7)\n",
      "[[1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]]\n",
      "shape: (7, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print('shape:', X_train.shape)\n",
    "print(y_train)\n",
    "print('shape:', y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 , [1. 0. 0.] , 0\n",
      "1 , [0. 0. 1.] , 2\n",
      "2 , [0. 0. 1.] , 2\n",
      "3 , [1. 0. 0.] , 0\n",
      "4 , [0. 0. 1.] , 2\n",
      "5 , [1. 0. 0.] , 0\n",
      "6 , [1. 0. 0.] , 0\n",
      "[1, 3, 3, 1, 3, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "twoDlist = []\n",
    "for x_i, x_v in enumerate(y_train):\n",
    "    for y_i, y_v in enumerate(x_v):\n",
    "#         print(x_i,',',x_v,',',y_i,',',y_v)\n",
    "        if x_v[y_i] == 1.0:\n",
    "            print(x_i,',',x_v,',',y_i)\n",
    "            if y_i == 0:\n",
    "                twoDlist.insert(x_i, 1)\n",
    "            elif y_i == 1:\n",
    "                twoDlist.insert(x_i, 2)\n",
    "            elif y_i == 2:\n",
    "                twoDlist.insert(x_i, 3)\n",
    "print(twoDlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [3],\n",
       "       [3],\n",
       "       [1],\n",
       "       [3],\n",
       "       [1],\n",
       "       [1]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = np.array(twoDlist)\n",
    "y_train = [[i] for i in y_train]\n",
    "y_train = np.array(y_train)\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train = [[i] for i in X_train]\n",
    "# X_train = np.array(X_train)\n",
    "# X_train\n",
    "X_train = X_train.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.63245553]\n",
      " [-0.63245553]\n",
      " [ 1.58113883]\n",
      " [-0.63245553]\n",
      " [-0.63245553]\n",
      " [-0.63245553]\n",
      " [ 1.58113883]]\n",
      "shape: (7, 1)\n",
      "[[1]\n",
      " [3]\n",
      " [3]\n",
      " [1]\n",
      " [3]\n",
      " [1]\n",
      " [1]]\n",
      "shape: (7, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print('shape:', X_train.shape)\n",
    "print(y_train)\n",
    "print('shape:', y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zawar.LAPTOP-L2O17FMQ\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='poly',\n",
       "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC(kernel = 'poly', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.70710678],\n",
       "       [-0.70710678],\n",
       "       [-1.41421356],\n",
       "       [ 1.41421356],\n",
       "       [ 0.70710678],\n",
       "       [-0.70710678]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test.reshape(-1,1)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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