01-introduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from preamble import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "## Introduction\n",
    "\n",
    "\n",
    "\n",
    "Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social network\n",
    "\n",
    "Many people think that machine learning can only be applied by large companies with extensive research teams. This book will show you how easy it can be to build machine learning solutions yourself, and how to best go about it\n",
    "\n",
    "The book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. This is an introductory book with focus on using Python and the scikit-learn library.\n",
    "\n",
    "This book is about practical aspects of using machine learning algorithms. As mathematics (probability theory, in particular) is the foundation upon which machine learning is built, the book will not go into the analysis of the algorithms in great detail\n",
    "\n",
    "We will also not describe how to write machine learning algorithms from scratch, and will instead focus on how to use the large array of models already implemented in scikit-learn and other libraries\n",
    "\n",
    "\n",
    "\n",
    "### What is Machine Learning?\n",
    "\n",
    "Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence, and computer science.\n",
    "\n",
    "It is also known as predictive analytics or statistical learning\n",
    "\n",
    "Put pic of books\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why Machine Learning?\n",
    "\n",
    "In the early days of “intelligent” applications, many systems used handcoded rules of “if ” and “else” decisions to process data or adjust to user input (e,g spam filter).\n",
    "\n",
    "For example, you could make up a blacklist of words that would result in an email being marked as spam ( any email containing \"Nigerian Price\" mark it as Spam)\n",
    "\n",
    "\n",
    "Manually crafting decision rules is feasible for some applications, particularly those in which humans have a good understanding of the process to model. \n",
    "\n",
    "However, using handcoded rules to make decisions has two major disadvantages:\n",
    "    \n",
    " \n",
    "\n",
    "> The logic required to make a decision is specific to a single domain and task. Changing the task even slightly might require a rewrite of the whole system. <br><br>\n",
    "\n",
    "> Designing rules requires a deep understanding of how a decision should be made by a human expert\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problems Machine Learning Can Solve\n",
    "\n",
    "**Supervised learning** the user provides the algorithm with pairs of inputs and desired outputs, and the algorithm finds a way to produce the desired output given an input. In particular, the algorithm is able to create an output for an input it has never seen before without any help from a human.\n",
    "\n",
    "While creating a dataset of inputs and outputs is often a laborious manual process, supervised learning algorithms are well understood and their performance is easy to measure.\n",
    "\n",
    "Examples of supervised machine learning tasks includes:  <br><br>\n",
    "    \n",
    "> Identifying the zip code from handwritten digits on an envelope <br><br>\n",
    "> Determining whether a tumor is benign based on a medical image <br><br>\n",
    "> Detecting fraudulent activity in credit card transactions\n",
    "\n",
    "\n",
    "\n",
    "In **unsupervised learning**, only the input data is known, and no known output data is given to the algorithm. While there are many successful applications of these methods, they are usually harder to understand and evaluate\n",
    "\n",
    "Examples of unsupervised learning include: <br><br>\n",
    "\n",
    "> Identifying topics in a set of blog posts <br><br>\n",
    "> Detecting abnormal access patterns to a website   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Knowing Your Task and Knowing Your Data\n",
    "\n",
    "The most important part in the machine learning process is understanding the data you are working with and how it relates to the task you want to solve.\n",
    "\n",
    "It is necessary to understand what is going on in your dataset before you begin building a model\n",
    "\n",
    "Each algorithm is different in terms of what kind of data and what problem setting it works best for\n",
    "\n",
    "While you are building a machine learning solution, you should answer, or at least keep in mind, the following questions\n",
    "\n",
    "\n",
    "> What question(s) am I trying to answer? Do I think the data collected can answer that question?\n",
    "\n",
    "> What is the best way to phrase my question(s) as a machine learning problem?\n",
    "\n",
    "> Have I collected enough data to represent the problem I want to solve?\n",
    "\n",
    "> What features of the data did I extract, and will these enable the right predictions?\n",
    "\n",
    "> How will I measure success in my application?\n",
    "\n",
    "> How will the machine learning solution interact with other parts of my research or business product?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why Python?\n",
    "\n",
    "Python has become the lingua franca for many data science applications. \n",
    "\n",
    "It combines the power of general-purpose programming languages with the ease of use of domain-specific scripting languages like MATLAB or R. \n",
    "\n",
    "Python has libraries for data loading, visualization, statistics, natural language processing, image processing, and more. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scikit-learn\n",
    "\n",
    "scikit-learn is an open source project, meaning that it is free to use and distribute, and anyone can easily obtain the source code to see what is going on behind the scence\n",
    "\n",
    "It contains a number of state-of-the-art machine learning algorithms, as well as comprehensive documentation about each algorithm. \n",
    "\n",
    "scikit-learn is a very popular tool, and the most prominent Python library for machine learning. It is widely used in industry and academia\n",
    "\n",
    "### Installing scikit-learn\n",
    "\n",
    "If you already have a Python installation set up, you can use pip or conda to install any package.\n",
    "\n",
    "Conda vs. pip vs. virtualenv commands\n",
    "\n",
    "> If you have used pip and virtualenv in the past, you can use conda to perform all of the same operations. Pip is a package manager and virtualenv is an environment manager. Conda is both. See the distinction here: https://docs.conda.io/projects/conda/en/latest/commands.html\n",
    " \n",
    "Anaconda Tutorial - Python Virtual Environment Manager: https://www.youtube.com/watch?v=mIB7IZFCE_k\n",
    "\n",
    "\n",
    "Why creates enviroment? Recommended to install Anaconda or miniconda:\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import mglearn\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn.model_selection.train_test_split\n",
    "\n",
    "#Throughout the book we make ample use of NumPy, matplotlib and pandas. All the code will assume the following imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(\"x:\\n{}\".format(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SciPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy array:\n",
      " [[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "# Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else\n",
    "eye = np.eye(4)\n",
    "print(\"NumPy array:\\n\", eye)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the NumPy array to a SciPy sparse matrix in CSR format\n",
    "# Only the nonzero entries are stored\n",
    "sparse_matrix = sparse.csr_matrix(eye)\n",
    "print(\"\\nSciPy sparse CSR matrix:\\n\", sparse_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.ones(4)\n",
    "row_indices = np.arange(4)\n",
    "col_indices = np.arange(4)\n",
    "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
    "print(\"COO representation:\\n\", eye_coo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa032a34400>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate a sequence of numbers from -10 to 10 with 100 steps in between\n",
    "x = np.linspace(-10, 10, 100)\n",
    "# Create a second array using sine\n",
    "y = np.sin(x)\n",
    "# The plot function makes a line chart of one array against another\n",
    "plt.plot(x, y, marker=\"x\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>New York</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>Paris</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linda</td>\n",
       "      <td>London</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Location  Age\n",
       "0   John  New York   24\n",
       "1   Anna     Paris   13\n",
       "2  Peter    Berlin   53\n",
       "3  Linda    London   33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# create a simple dataset of people\n",
    "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
    "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
    "        'Age' : [24, 13, 53, 33]\n",
    "       }\n",
    "\n",
    "data_pandas = pd.DataFrame(data)\n",
    "# IPython.display allows \"pretty printing\" of dataframes\n",
    "# in the Jupyter notebook\n",
    "display(data_pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select all rows that have an age column greater than 30\n",
    "display(data_pandas[data_pandas.Age > 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mglearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python 2 versus Python 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Versions Used in this Book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "print(\"Python version:\", sys.version)\n",
    "\n",
    "import pandas as pd\n",
    "print(\"pandas version:\", pd.__version__)\n",
    "\n",
    "import matplotlib\n",
    "print(\"matplotlib version:\", matplotlib.__version__)\n",
    "\n",
    "import numpy as np\n",
    "print(\"NumPy version:\", np.__version__)\n",
    "\n",
    "import scipy as sp\n",
    "print(\"SciPy version:\", sp.__version__)\n",
    "\n",
    "import IPython\n",
    "print(\"IPython version:\", IPython.__version__)\n",
    "\n",
    "import sklearn\n",
    "print(\"scikit-learn version:\", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Project Steps\n",
    "\n",
    "At a basic level, these are some of the steps to do a machine project\n",
    "\n",
    "- Frame the problem\n",
    "- Get the data.\n",
    "- Process the data: explore the data and prepare the data\n",
    "- Model the data/Train the Model\n",
    "- Evaluate the Model\n",
    "- Make Predictions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem: Classifying Iris Species\n",
    "\n",
    "Let’s assume that a hobby botanist is interested in distinguishing the species of some iris flowers that she has found. She has collected some measurements associated with each iris: \n",
    "\n",
    "- the length and width of the petals and  <br><br>\n",
    "- the length and width of the sepals, all measured in centimeters.\n",
    "\n",
    "Our goal is to build a machine learning model that can learn from the measurements of these irises whose species is known, so that we can predict the species for a new iris.\n",
    "\n",
    "\n",
    "![sepal_petal](images/iris_petal_sepal.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question\n",
    "\n",
    "> is this Supervised or Unsupervised? <br><br>\n",
    "\n",
    "> is this classification or regression task?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Supervied \n",
    "\n",
    "> Because we have measurements for which we know the correct species of iris, this is a supervised learning problem. In this problem, we want to predict one of several options (the species of iris). \n",
    " <br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Classification\n",
    "\n",
    "> This is an example of a classification problem. This is a three-class classification problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the Data\n",
    "\n",
    "> Create your dataset, Google Dataset, UCI ML repo, Kaggle,and Github <br><br>\n",
    "> There are 150 observations with 4 features each (sepal length, sepal width, petal length, petal width). <br><br>\n",
    "> There are no null values, so we don't have to worry about that. <br><br>\n",
    "> There are 50 observations of each species (setosa, versicolor, virginica). <br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv(\"data/iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepalLength  sepalWidth  petalLength  petalWidth        class\n",
       "0          5.1         3.5          1.4         0.2  Iris-setosa\n",
       "1          4.9         3.0          1.4         0.2  Iris-setosa\n",
       "2          4.7         3.2          1.3         0.2  Iris-setosa\n",
       "3          4.6         3.1          1.5         0.2  Iris-setosa\n",
       "4          5.0         3.6          1.4         0.2  Iris-setosa"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let us learn some Terminology\n",
    "Since many scientific domains have contributed to this field, and often called with diffrent names, there are synonyms for different entities:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Nomenclature:  “machine learning,”“artificial intelligence,” “pattern recognition,” “data mining,” “predictive analytics,” and “knowledge discovery\" <br><br>\n",
    "- The terms \"sample\", \"data point\", \"observation\",or \"instance\" refer to a single, independent unit of data,such as a customer, patient, or a single flower. <br><br>\n",
    "- Training set and Validation Set: The \"training set\" consists of the data used to develop models while the test or \"validation sets\" are used solely for evaluating the performance of a final set of candidate models <br><br>\n",
    "- The \"predictors\", \"independent variables\", \"attributes\",or \"descriptors\" are the data used as input for the prediction equation. In this example there are four predictors (SepalLength, SepalWith, PetalLenght, PetalWidth)<br><br>\n",
    "- The Outcome, dependent variable, target, class,corresponse refer to the outcome event or quantity that is being predicted. In this example, we have three (3) outcomes (setosa, versicolor, virginica) <br><br>\n",
    "- Continuous data have natural, numeric scales. Example predicting the cost of a house, predicting blood presure all have continues data. Our predictors in this example are continuous data (SepalLength, SepalWith, PetalLenght, PetalWidth) <br><br>\n",
    "- Categorical data, otherwise known as nominal, attribute,or discrete data, take on specific values that have no scale. Examples include Credit status (“good” or “bad”) or color (“red,”“blue,” etc.). Our output class in this example is categorical data (setosa, versicolor, virginica) <br><br>\n",
    "- Model building, model training,and parameter estimation all refer to the process of using data to determine values of model equations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process the Data \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Data exploration/ Data Understanding\n",
    "\n",
    "Once you get your hands on the data for the first time, you would want to know its structure (number of files, rows, columns etc.), understand how they are related to each other and whether something looks unusual. This is also commonly known as Exploratory data analysis (EDA).\n",
    "\n",
    "- Visualizing the data to check if there are correlations. It will be necessary to make a selection of characteristics since the ones you choose will directly impact the execution times and the results. You can also reduce dimensions by applying PCA if necessary. <br><br>\n",
    "- If data is having some missing values then dealing with the missing values by either calculating the mean, median or mode of the feature and replace it with the missing values or dropping the rows. <br><br>\n",
    "- Data may have some unnecessary columns like Serial Number which is unique for each user, so we may delete these columns if not required in our analysis. <br><br>\n",
    "- Scaling and Normalizing the data <br><br>\n",
    "- Balance the amount of data we have for each result -class- so that it is significant as the learning may be biased towards a type of response and when your model tries to generalize knowledge it will fail <br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepalLength  sepalWidth  petalLength  petalWidth           class\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iris.head()\n",
    "iris.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many observations do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# len(iris)\n",
    "# iris.shape[0]\n",
    "len(iris.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many predictors, independent variables do we have? ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepalLength', 'sepalWidth', 'petalLength', 'petalWidth', 'class'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(iris.columns)  # Analogous to len(iris.index), len(df.columns)\n",
    "iris.shape[1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many target, output or dependent variables do we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: class, Length: 150, dtype: object"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['class']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['class'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding general information about pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   sepalLength  150 non-null    float64\n",
      " 1   sepalWidth   150 non-null    float64\n",
      " 2   petalLength  150 non-null    float64\n",
      " 3   petalWidth   150 non-null    float64\n",
      " 4   class        150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas describ() method to see some statistics about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepalLength  sepalWidth  petalLength  petalWidth\n",
       "count   150.000000  150.000000   150.000000  150.000000\n",
       "mean      5.843333    3.054000     3.758667    1.198667\n",
       "std       0.828066    0.433594     1.764420    0.763161\n",
       "min       4.300000    2.000000     1.000000    0.100000\n",
       "25%       5.100000    2.800000     1.600000    0.300000\n",
       "50%       5.800000    3.000000     4.350000    1.300000\n",
       "75%       6.400000    3.300000     5.100000    1.800000\n",
       "max       7.900000    4.400000     6.900000    2.500000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Visualizing your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Inspecting your data is a good way to find abnormalities and peculiarities. Maybe some of your irises were measured using inches and not centimeters <br><br>\n",
    "- One of the best ways to inspect data is to visualize it. One way to do this is by using a scatter plot. A scatter plot of the data puts one feature along the x-axis and another along the y-axis, and draws a dot for each data point. <br><br>\n",
    "\n",
    "- Unfortunately, computer screens have only two dimensions, which allows us to plot only two ( or maybe three) features at a time. It is difficult to plot datasets with more than three features this way. One way around this problem is to do a pair plot, which looks at all possible pairs of features. If you have a small number of features, such as the four we have here, this is quite reasonable. You should keep in mind, however, that a pair plot does not show the interaction of all of features at once, so some interesting aspects of the data may not be revealed when visualizing it this way. <br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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",
      "text/plain": [
       "<Figure size 823.25x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "sns.set_palette('husl')\n",
    "\n",
    "g = sns.pairplot(iris, hue='class', markers='+')\n",
    "plt.show()\n",
    "\n",
    "#https://seaborn.pydata.org: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the plots, we can see that the three classes seem to be relatively well separated using the sepal and petal measurements. This means that a machine learning model will likely be able to learn to separate them.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After graphing the features in a pair plot, it is clear that the relationship between pairs of features of a iris-setosa (in pink) is distinctly different from those of the other two species. There is some overlap in the pairwise relationships of the other two species, iris-versicolor (brown) and iris-virginica (green)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the data:  Training and Testing Data\n",
    "\n",
    "> we cannot use the data we used to build the model to evaluate it. This is because our model can always simply remember the whole training set, and will therefore always predict the correct label for any point in the training set. This “remembering” does not indicate to us whether our model will generalize well (in other words, whether it will also perform well on new data\n",
    "\n",
    "How to To assess the model’s performance ?\n",
    "\n",
    ">  we show it new data (data that it hasn’t seen before) for which we have labels. This is usually done by splitting the labeled data we have collected (here, our 150 flower measurements) into two parts. One part of the data is used to build our machine learning model, and is called the **training data** or **training set**. The rest of the data will be used to assess how well the model works; this is called the **test data**, **test set**, or **hold-out set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How can we divide our dataset into train set and test set?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    ">Fortunately, scikit-learn contains a function that shuffles the dataset and splits it for you: the **train_test_split** function. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, how Sklearn divide dataset? \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/train_test_split_sk.svg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepalLength  sepalWidth  petalLength  petalWidth           class\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, let us creates train set and test set of our dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: class, Length: 150, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris.drop(['class'], axis=1) # predictors\n",
    "y = iris['class']                # target or class\n",
    "# # print(X.head())\n",
    "# print(X.shape)\n",
    "# # print(y.head())\n",
    "# print(y.shape)\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.4, random_state=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (90, 4)\n",
      "y_train shape: (90,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape: (60, 4)\n",
      "y_test shape: (60,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82     Iris-versicolor\n",
       "134     Iris-virginica\n",
       "114     Iris-virginica\n",
       "42         Iris-setosa\n",
       "109     Iris-virginica\n",
       "57     Iris-versicolor\n",
       "1          Iris-setosa\n",
       "70     Iris-versicolor\n",
       "25         Iris-setosa\n",
       "84     Iris-versicolor\n",
       "66     Iris-versicolor\n",
       "133     Iris-virginica\n",
       "102     Iris-virginica\n",
       "107     Iris-virginica\n",
       "26         Iris-setosa\n",
       "23         Iris-setosa\n",
       "123     Iris-virginica\n",
       "130     Iris-virginica\n",
       "21         Iris-setosa\n",
       "12         Iris-setosa\n",
       "71     Iris-versicolor\n",
       "128     Iris-virginica\n",
       "48         Iris-setosa\n",
       "72     Iris-versicolor\n",
       "88     Iris-versicolor\n",
       "148     Iris-virginica\n",
       "74     Iris-versicolor\n",
       "96     Iris-versicolor\n",
       "63     Iris-versicolor\n",
       "132     Iris-virginica\n",
       "39         Iris-setosa\n",
       "53     Iris-versicolor\n",
       "79     Iris-versicolor\n",
       "10         Iris-setosa\n",
       "50     Iris-versicolor\n",
       "49         Iris-setosa\n",
       "43         Iris-setosa\n",
       "135     Iris-virginica\n",
       "40         Iris-setosa\n",
       "115     Iris-virginica\n",
       "142     Iris-virginica\n",
       "69     Iris-versicolor\n",
       "17         Iris-setosa\n",
       "46         Iris-setosa\n",
       "54     Iris-versicolor\n",
       "126     Iris-virginica\n",
       "61     Iris-versicolor\n",
       "124     Iris-virginica\n",
       "117     Iris-virginica\n",
       "20         Iris-setosa\n",
       "146     Iris-virginica\n",
       "35         Iris-setosa\n",
       "6          Iris-setosa\n",
       "15         Iris-setosa\n",
       "28         Iris-setosa\n",
       "97     Iris-versicolor\n",
       "56     Iris-versicolor\n",
       "81     Iris-versicolor\n",
       "98     Iris-versicolor\n",
       "149     Iris-virginica\n",
       "Name: class, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Your First Model: k-Nearest Neighbors\n",
    "\n",
    "- Now we can start building the actual machine learning model.  <br><br>\n",
    "\n",
    "- There are many classification algorithms in scikit-learn that we could use (https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html).  <br><br>\n",
    "  \n",
    "- Here we will use a k-nearest neighbors classifier, which is easy to understand(https://scikit-learn.org/stable/modules/neighbors.html) <br><br>\n",
    " \n",
    "-  All machine learning models in scikit-learn are implemented in their own classes, which are called Estimator classes. Look at diff estimators: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html <br><br>\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/threclass_plot_classification_001.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The k-nearest neighbors classification algorithm is implemented in the KNeighborsClassifier class in the neighbors module\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# For KNN models, the value of k determines the level of complexity. A lower value of k means that the model is more complex.\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "#Before we can use the model, we need to instantiate the class into an object. This is when we will set any parameters of the model. The most important parameter of KNeighbor sClassifier is the number of neighbors, which we will set to 1:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The knn object encapsulates the algorithm that will be used to build the model from the training data, as well the algorithm to make predictions on new data points. It will also hold the information that the algorithm has extracted from the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mType:\u001b[0m        KNeighborsClassifier\n",
      "\u001b[0;31mString form:\u001b[0m KNeighborsClassifier(n_neighbors=1)\n",
      "\u001b[0;31mFile:\u001b[0m        ~/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\n",
      "\u001b[0;31mDocstring:\u001b[0m  \n",
      "Classifier implementing the k-nearest neighbors vote.\n",
      "\n",
      "Read more in the :ref:`User Guide <classification>`.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "n_neighbors : int, default=5\n",
      "    Number of neighbors to use by default for :meth:`kneighbors` queries.\n",
      "\n",
      "weights : {'uniform', 'distance'} or callable, default='uniform'\n",
      "    weight function used in prediction.  Possible values:\n",
      "\n",
      "    - 'uniform' : uniform weights.  All points in each neighborhood\n",
      "      are weighted equally.\n",
      "    - 'distance' : weight points by the inverse of their distance.\n",
      "      in this case, closer neighbors of a query point will have a\n",
      "      greater influence than neighbors which are further away.\n",
      "    - [callable] : a user-defined function which accepts an\n",
      "      array of distances, and returns an array of the same shape\n",
      "      containing the weights.\n",
      "\n",
      "algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'\n",
      "    Algorithm used to compute the nearest neighbors:\n",
      "\n",
      "    - 'ball_tree' will use :class:`BallTree`\n",
      "    - 'kd_tree' will use :class:`KDTree`\n",
      "    - 'brute' will use a brute-force search.\n",
      "    - 'auto' will attempt to decide the most appropriate algorithm\n",
      "      based on the values passed to :meth:`fit` method.\n",
      "\n",
      "    Note: fitting on sparse input will override the setting of\n",
      "    this parameter, using brute force.\n",
      "\n",
      "leaf_size : int, default=30\n",
      "    Leaf size passed to BallTree or KDTree.  This can affect the\n",
      "    speed of the construction and query, as well as the memory\n",
      "    required to store the tree.  The optimal value depends on the\n",
      "    nature of the problem.\n",
      "\n",
      "p : int, default=2\n",
      "    Power parameter for the Minkowski metric. When p = 1, this is\n",
      "    equivalent to using manhattan_distance (l1), and euclidean_distance\n",
      "    (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.\n",
      "\n",
      "metric : str or callable, default='minkowski'\n",
      "    the distance metric to use for the tree.  The default metric is\n",
      "    minkowski, and with p=2 is equivalent to the standard Euclidean\n",
      "    metric. See the documentation of :class:`DistanceMetric` for a\n",
      "    list of available metrics.\n",
      "    If metric is \"precomputed\", X is assumed to be a distance matrix and\n",
      "    must be square during fit. X may be a :term:`sparse graph`,\n",
      "    in which case only \"nonzero\" elements may be considered neighbors.\n",
      "\n",
      "metric_params : dict, default=None\n",
      "    Additional keyword arguments for the metric function.\n",
      "\n",
      "n_jobs : int, default=None\n",
      "    The number of parallel jobs to run for neighbors search.\n",
      "    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
      "    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
      "    for more details.\n",
      "    Doesn't affect :meth:`fit` method.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "classes_ : array of shape (n_classes,)\n",
      "    Class labels known to the classifier\n",
      "\n",
      "effective_metric_ : str or callble\n",
      "    The distance metric used. It will be same as the `metric` parameter\n",
      "    or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to\n",
      "    'minkowski' and `p` parameter set to 2.\n",
      "\n",
      "effective_metric_params_ : dict\n",
      "    Additional keyword arguments for the metric function. For most metrics\n",
      "    will be same with `metric_params` parameter, but may also contain the\n",
      "    `p` parameter value if the `effective_metric_` attribute is set to\n",
      "    'minkowski'.\n",
      "\n",
      "n_samples_fit_ : int\n",
      "    Number of samples in the fitted data.\n",
      "\n",
      "outputs_2d_ : bool\n",
      "    False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit\n",
      "    otherwise True.\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> X = [[0], [1], [2], [3]]\n",
      ">>> y = [0, 0, 1, 1]\n",
      ">>> from sklearn.neighbors import KNeighborsClassifier\n",
      ">>> neigh = KNeighborsClassifier(n_neighbors=3)\n",
      ">>> neigh.fit(X, y)\n",
      "KNeighborsClassifier(...)\n",
      ">>> print(neigh.predict([[1.1]]))\n",
      "[0]\n",
      ">>> print(neigh.predict_proba([[0.9]]))\n",
      "[[0.66666667 0.33333333]]\n",
      "\n",
      "See Also\n",
      "--------\n",
      "RadiusNeighborsClassifier\n",
      "KNeighborsRegressor\n",
      "RadiusNeighborsRegressor\n",
      "NearestNeighbors\n",
      "\n",
      "Notes\n",
      "-----\n",
      "See :ref:`Nearest Neighbors <neighbors>` in the online documentation\n",
      "for a discussion of the choice of ``algorithm`` and ``leaf_size``.\n",
      "\n",
      ".. warning::\n",
      "\n",
      "   Regarding the Nearest Neighbors algorithms, if it is found that two\n",
      "   neighbors, neighbor `k+1` and `k`, have identical distances\n",
      "   but different labels, the results will depend on the ordering of the\n",
      "   training data.\n",
      "\n",
      "https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm\n"
     ]
    }
   ],
   "source": [
    "knn?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To build the model on the training set, we call the **fit** method of the knn object, which takes as arguments the NumPy array X_train containing the training data and the NumPy array y_train of the corresponding training labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1) # create an object\n",
    "knn.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The fit method returns the knn object itself (and modifies it in place), so we get a string representation of our classifier. <br><br>\n",
    "\n",
    "- The representation shows us which parameters were used in creating the model. Nearly all of them are the default values, but you can also find n_neighbors=1, which is the parameter that we passed. \n",
    "<br><br>\n",
    "- Most models in scikit-learn have many parameters, but the majority of them are either speed opti‐ mizations or for very special use cases <br><br>\n",
    "- We now train or model. But, how good it mode? <br><br>\n",
    "- So, after we train a model, we need to find out if the model can do perform well on test data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Model\n",
    "\n",
    "- This is where the test set that we created earlier comes in <br><br>\n",
    "- The test data was not used to build the model, but we do know what the correct species is for each iris in the test set. <br><br>\n",
    "- Therefore, we can make a prediction for each iris in the test data and compare it against its true label (the known species).  <br><br>\n",
    "- We can then measure how well the model works by computing the accuracy, which is the fraction of flowers for which the right species was predicted: <br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set predictions:\n",
      " ['Iris-versicolor' 'Iris-virginica' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(\"Test set predictions:\\n\", y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score is: 0.97\n"
     ]
    }
   ],
   "source": [
    "test_score = knn.score(X_test, y_test)\n",
    "print(f\"Test set score is: {test_score:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For this model, the test set accuracy is about 0.97, which means we made the right prediction for 97% of the irises in the test set. Under some mathematical assumptions, this means that we can expect our model to be correct 97% of the time for new irises. For our hobby botanist application, this high level of accuracy means that our model may be trustworthy enough to use. In later chapters we will discuss how we can improve performance, and what caveats there are in tuning a mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Predictions\n",
    "\n",
    "- We can now make predictions using this model on new data for which we might not know the correct labels. <br><br>\n",
    "\n",
    "- Imagine we found an iris in the wild with a sepal length of 5 cm, a sepal width of 2.9 cm, a petal length of 1 cm, and a petal width of 0.2 cm. What species of iris would this be? <br><br>\n",
    "\n",
    "- We can put this data into a NumPy array, again by calculating the shape—that is, the number of samples (1) multiplied by the number of features (4): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_new.shape: (1, 4)\n"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[5, 2.9, 1, 0.2]]) \n",
    "print(\"X_new.shape: {}\".format(X_new.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note that we made the measurements of this single flower into a row in a twodimensional NumPy array, as scikit-learn always expects two-dimensional arrays for the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make a prediction, we call the predict method of the knn object:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = knn.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: ['Iris-setosa']\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction:\", prediction)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our model predicts that this new iris belong to species setosa species. <br><br>\n",
    "- But how do we know whether we can trust our model? We don’t know the correct species of this sample, which is the whole point of building the model <br><br>\n",
    "- We need to evaluate our model after prediction to evaluate its performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and Outlook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Frame the problem\n",
    "- Get the data\n",
    "- Process the data: explore the data and prepare the data\n",
    "- Model the data/Train the Model\n",
    "- Evaluate the Model\n",
    "- Make Predictions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "test_score = knn.score(X_test, y_test)\n",
    "print(f\"Test set score is: {test_score:.2f}\")\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}