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‎SVM_wine_data.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# importing the modules"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#importin the library from the pip\n",
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"#installing libraries \n",
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"#pip3 install numpy, pandas, scikit\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn import datasets"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Loading the Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#this is the import dataset from the scikit learn\n",
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"wine = datasets.load_wine()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Features and Labels"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#here x denotes to the Features For the Data\n",
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"X = wine.data\n",
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"#here y denotes to the Labels for the data\n",
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"\"\"\"target is the labels for the data it consists of the classes or the prediction values\"\"\"\n",
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"y = wine.target"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Train_Test_Split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"\"\"\"here we will separate the into the parts train part and the test part and into the split part of the data\n",
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"X_train, y_train consists of the only training features and the labels Example:- train_size = 0.8 it will consider \n",
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"80 persent training and 20 persent test (X_test, y_test)\"\"\"\n",
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Standerlization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"\"\"\"Here we are going to discuss about the scaling techniques the main important scaling technique is StandardScaler\n",
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"which will allow Data in Between the [1, 0] Tis is one of the most import preprocessing technique\"\"\"\n",
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"\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"sc = StandardScaler()\n",
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"X_train = sc.fit_transform(X_train)\n",
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"X_test = sc.transform(X_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# SVM(Support Vector Machine)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
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" decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
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" kernel='rbf', max_iter=-1, probability=False, random_state=0,\n",
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" shrinking=True, tol=0.001, verbose=False)"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"we are importing Support vector Machine from the Scikit Learn \n",
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"Present we are working with SVC(Support Vector Classifier) \n",
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"C is the most important parameter which says about the regularization and create good Hyper perameter line \n",
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"in the algorithm and also know as Penalty parameter C of the error term, random_state is the parameter which will use as the seed function it will work with \n",
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"random numbers , Kernel is the used to use for to solve non-linear complex dimention(Features) in the data set\n",
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"degree is used for only the poly nomial kernels , (rbf, linear, poly, sigmoid)\"\"\"\n",
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"from sklearn.svm import SVC\n",
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"ppn = SVC(C=1, random_state = 0)\n",
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"ppn.fit(X_train,y_train)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# predicting"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"y_pred = ppn.predict(X_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# misscalssification"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"(y_pred != y_test).sum()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Accuracy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.metrics import accuracy_score\n",
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"accuracy_score(y_test, y_pred)"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {
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"collapsed": true
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},
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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