Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with the Iris Dataset\n",
    "\n",
    "* What is the famous iris dataset, and how does it relate to machine learning?\n",
    "* How do we load the iris dataset into scikit-learn?\n",
    "* How do we describe a dataset using machine learning terminology?\n",
    "* What are scikit-learn's four key requirements for working with data?\n",
    "\n",
    "## Introducing the Iris dataset\n",
    "\n",
    "![Iris](images/iris.png)\n",
    "\n",
    "* 50 samples of 3 different species of iris (150 samples total)\n",
    "* Measurements: sepal length, sepal width, petal length, petal width\n",
    "\n",
    "## Objective\n",
    "**Predict the species of an iris using the measurements**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# First, we'll import pandas, a data processing and CSV file I/O library\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "# We'll also import seaborn, a Python graphing library\n",
    "import warnings # current version of seaborn generates a bunch of warnings that we'll ignore\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"white\", color_codes=True)\n",
    "\n",
    "# Next, we'll load the Iris flower dataset, which is in the \"../input/\" directory\n",
    "iris = pd.read_csv(\"iris.csv\", header=None) # the iris dataset is now a Pandas DataFrame\n",
    "\n",
    "# Let's see what's in the iris data - Jupyter notebooks print the result of the last thing you do\n",
    "iris.head()\n",
    "\n",
    "# Press shift+enter to execute this cell\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simplify work, we'll have to label the columns of the dataset.\n",
    "\n",
    "## Attribute Information:\n",
    "   1. sepal length in cm\n",
    "   2. sepal width in cm\n",
    "   3. petal length in cm\n",
    "   4. petal width in cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris.columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species']\n",
    "\n",
    "iris = shuffle(iris)\n",
    "\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's see how many examples we have of each species\n",
    "iris[\"Species\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We'll use seaborn's FacetGrid to color the scatterplot by species\n",
    "sns.FacetGrid(iris, hue=\"Species\", size=5) \\\n",
    "   .map(plt.scatter, \"SepalLengthCm\", \"SepalWidthCm\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label Encoding\n",
    "\n",
    "Since the Species feature is not numerical i.e. the values are text based eg \"iris-setosa\", we need to convert them into integers so that the model can understand what it is working with. This type of data is called **categorical data**\n",
    "\n",
    "There are several methods of encoding the categorical data into a format that the model can easily process:\n",
    "* Scikit-learn LabelEncoder\n",
    "* Scikit-learn OneHotEncoder\n",
    "* Pandas get_dummies\n",
    "\n",
    "##Preparing X and y using pandas\n",
    "* scikit-learn expects X (feature matrix) and y (response vector) to be NumPy arrays.\n",
    "* However, pandas is built on top of NumPy.\n",
    "* Thus, X can be a pandas DataFrame and y can be a pandas Series!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(iris['Species'])\n",
    "y = le.transform(iris['Species'])\n",
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# list the classes\n",
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get the features\n",
    "X = iris.drop('Species', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting X and y into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the species of the test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}