pandas_sklearn_introduction.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load library with keywords as you want! \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read files with pandas\n",
    "data = pd.read_table(\"data/sms.tsv\", names = [\"type\", \"message\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# view of the data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# summary of the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# export data to other formats! \n",
    "data.to_csv(\"data/sms.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del data\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn\n",
    "\n",
    "* Iris Dataset: http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html\n",
    "* Wiki Description: https://en.wikipedia.org/wiki/Iris_flower_data_set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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   "source": [
    "# load the iris dataset as an example\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# store the feature matrix (X) and response vector (y)\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\"Features\"** are also known as predictors, inputs, or attributes. The **\"response\"** is also known as the target, label, or output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# check the shapes of X and y\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\"Observations\"** are also known as samples, instances, or records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# examine the first 5 rows of the feature matrix (including the feature names)\n",
    "pd.DataFrame(X, columns=iris.feature_names).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# examine the response vector\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to **build a model**, the features must be **numeric**, and every observation must have the **same features in the same order**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import the class\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# instantiate the model (with the default parameters)\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# fit the model with data (occurs in-place)\n",
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# predict the response for a new observation\n",
    "knn.predict([[3, 5, 4, 2]])"
   ]
  }
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