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‎Classification with SVM/Iris_data_classification_implementing_SVM.ipynb

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{
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"cells": [
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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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"outputs": [],
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"source": [
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"# The Iris Setosa\n",
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"from IPython.display import Image\n",
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"url = 'http://upload.wikimedia.org/wikipedia/commons/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg'\n",
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"Image(url,width=400, height=300)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# The Iris Versicolor\n",
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"from IPython.display import Image\n",
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"url = 'http://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg'\n",
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"Image(url,width=400, height=300)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# The Iris Virginica\n",
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"from IPython.display import Image\n",
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"url = 'http://upload.wikimedia.org/wikipedia/commons/9/9f/Iris_virginica.jpg'\n",
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"Image(url,width=400, height=300)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"iris = sns.load_dataset('iris')\n",
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"iris.head(5)"
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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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"\n",
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"## Exploratory Data Analysis\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Setosa is the most separable. \n",
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"sns.pairplot(iris,hue='species',palette='Dark2')"
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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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"**Create a kde plot of sepal_length versus sepal width for setosa species of flower.**"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"setosa = iris[iris['species']=='setosa']\n",
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"sns.kdeplot( setosa['sepal_width'], setosa['sepal_length'],\n",
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" cmap=\"Spectral\", shade=True, shade_lowest=False)"
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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\n",
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"\n",
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"** Split your data into a training set and a testing set.**"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = iris.drop('species',axis=1)\n",
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"y = iris['species']\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
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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 a Model\n",
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"\n",
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"Now its time to train a Support Vector Machine Classifier. \n",
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"\n",
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"**Call the SVC() model from sklearn and fit the model to the training 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.svm import SVC"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"svc_model = SVC()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"svc_model.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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"collapsed": true
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},
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"source": [
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"## Model Evaluation\n",
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"\n",
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"**Now get predictions from the model and create a confusion matrix and a classification report.**"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictions = svc_model.predict(X_test)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import classification_report,confusion_matrix"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(confusion_matrix(y_test,predictions))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(classification_report(y_test,predictions))"
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]
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},
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{
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"cell_type": "markdown",
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"m F42D etadata": {},
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"source": [
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"## Gridsearch Practice\n",
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"\n",
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"** Import GridsearchCV from SciKit Learn.**"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import GridSearchCV"
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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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"**Create a dictionary called param_grid and fill out some parameters for C and gamma.**"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001]} "
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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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"** Create a GridSearchCV object and fit it to the training 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)\n",
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"grid.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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"** Now take that grid model and create some predictions using the test set and create classification reports and confusion matrices for them. Were you able to improve?**"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"grid_predictions = grid.predict(X_test)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(confusion_matrix(y_test,grid_predictions))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(classification_report(y_test,grid_predictions))"
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]
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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": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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