Iris-project-with-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, you will be introduced to build a simple linear regression model to predict flowers type. We will be covering the following topics:\n",
    "\n",
    "1. Intrduction to the dataset\n",
    "2. How to load dataset using Pandas\n",
    "3. How to split the dataset into training and validation\n",
    "4. How to train the model in a couple lines of code\n",
    "5. Plot the dataset\n",
    "\n",
    "In this tutorial, we are going to use **Logistic Regression** for training our model. As a beginner tutorial, we are not going to cover much details of how logistic regression works. Feel free to read more information about it here: [ https://en.wikipedia.org/wiki/Logistic_regression ]. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to the iris dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant\n",
    "\n",
    "The dataset has four features:\n",
    "* sepal_length\n",
    "* sepal_width\n",
    "* petal_length\n",
    "* petal_width\n",
    "\n",
    "Each plant has been measured on those 4 features and the species is recorded.\n",
    "The question is, if you see a new plant in the field, could you make a prediction of its species based on these meassurements?\n",
    "\n",
    "The data is saved in a .csv file.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"resources/three-kinds.png\" width=\"700\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"resources/Iris-image.png\" width=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"resources/raw-data.png\" width=\"400\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset using Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('iris.csv') # Load data to iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, **head( )** method returns the top 5 rows of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "    }\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</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>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>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>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>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>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head() # If you want to see more of data, say 20 lines, simply use iris.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**info( )** method lists the data type of each columns, number of non-missing values, and memory usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()\n",
    "#iris.dropna() will remove rows with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "#Return unique values of Series 'species'\n",
    "categories = iris['species'].unique()\n",
    "print(categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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": [
       "       sepal_length  sepal_width  petal_length  petal_width\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split dataset into training set and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.iloc[:, 0:4] #all the rows, and column 0 to column 3\n",
    "y = iris.iloc[:, 4]#all the row, and column 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 4 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "X.info() # only works for DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X's type: <class 'pandas.core.frame.DataFrame'>\n",
      "Y's type: <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(\"X's type: \" + str(type(X)))\n",
    "print(\"Y's type: \" + str(type(y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  # Split arrays or matrices into random train and test subsets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "# test_size should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split\n",
    "# random_state: random seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train's shape(120, 4)\n",
      "y_train's shape(120,)\n",
      "X_test's shape(30, 4)\n",
      "y_test's shape(30,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train's shape\" + str(X_train.shape))\n",
    "print(\"y_train's shape\" + str(y_train.shape))\n",
    "print(\"X_test's shape\" + str(X_test.shape))\n",
    "print(\"y_test's shape\" + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression # Our base model to use\n",
    "from sklearn import metrics # For model evaluation, qualify the quality of predictions\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear', multi_class='auto') # For small datasets, 'liblinear' is a good choice\n",
    "logreg.fit(X_train, y_train) # Present in sklearn.linear_model.logistic.LogisticRegression module\n",
    "\n",
    "y_pred = logreg.predict(X_test) #Predict class labels for samples in X_test.\n",
    "print(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"resources/logisticR.png\" width=\"500\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  0  6]]\n",
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(logreg.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# seperate each category\n",
    "setosa = iris[iris[\"species\"]==\"setosa\"]\n",
    "versicolor = iris[iris[\"species\"]==\"versicolor\"]\n",
    "verginica = iris[iris[\"species\"]==\"virginica\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(setosa.values[:, 0], setosa.values[:, 1],c=\"red\" )\n",
    "plt.scatter(versicolor.values[:, 0], versicolor.values[:, 1], c=\"blue\")\n",
    "plt.scatter(verginica.values[:,0], verginica.values[:,1], c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://archive.ics.uci.edu/ml/datasets/iris<br>\n",
    "* \"Python Data Analytics: With Pandas, NumPy, and Matplotlib\" 2ed by Fabio Nelli.\n",
    "* https://www.datacamp.com/community/tutorials/logistic-regression-R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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