report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Iris Flower Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning, Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](logo.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Table of Contents\n",
    "\n",
    "- [Getting Started](#Getting-Started)\n",
    "- [Load the Data](#Load-The-Data)\n",
    " - [Version Check-In](#Version-Check-In)\n",
    " - [Import Library](#Import-Library)\n",
    " - [Data Is Here](#Data-is-Here)\n",
    "- [Data Exploration](#Data-Exploration)\n",
    " - [Peak at Data](#Peak-at-Data)\n",
    " - [Statistical Summary](#Statistical-Summary)\n",
    "- [Data Visualization](#Data-Visualization)\n",
    " - [Univariate Plots](#Univariate-Plots)\n",
    " - [Multivariate Plots](#Multivariate-Plots)\n",
    "- [Evaluate Algorithms](#Evaluate-Algorithms)\n",
    " - [Create a Validation Dataset](#Create-a-Validation-Dataset)\n",
    " - [Developing Model](#Developing-Model)\n",
    "- [Make Prediction](#Make-Prediction)\n",
    " \n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Version Check-In"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Library Version check-in\n",
    "import sys, numpy, scipy, pandas as pd, matplotlib, sklearn, seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.7.0 (default, Jun 28 2018, 08:04:48) [MSC v.1912 64 bit (AMD64)]\n",
      "scipy: 1.1.0\n",
      "numpy: 1.15.2\n",
      "pandas: 0.23.4\n",
      "sklearn: 0.20.0\n",
      "matplotlib: 3.0.0\n",
      "Seaborn: 0.9.0\n"
     ]
    }
   ],
   "source": [
    "print('Python: {}'.format(sys.version))\n",
    "print('scipy: {}'.format(scipy.__version__))\n",
    "print('numpy: {}'.format(numpy.__version__))\n",
    "print('pandas: {}'.format(pd.__version__))\n",
    "print('sklearn: {}'.format(sklearn.__version__))\n",
    "print('matplotlib: {}'.format(matplotlib.__version__))\n",
    "print('Seaborn: {}'.format(sns.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No Warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No warning of any kind please!\n",
    "import warnings\n",
    "# will ignore any warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading required Libraries\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data is Here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset from UCI\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "\n",
    "# column names for the dataset\n",
    "names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']\n",
    "\n",
    "# feeding the data with pandas, giving column names to dataset. \n",
    "dataset = pd.read_csv(url, names= names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peak at Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width        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",
       "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
       "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
       "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
       "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
       "9           4.9          3.1           1.5          0.1  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Peak at the data\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has  150  rows and  5  columns.\n"
     ]
    }
   ],
   "source": [
    "# dimensions of the dataset\n",
    "r, c = dataset.shape\n",
    "print('This dataset has ',r,' rows and ' ,c,' columns.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping by Class\n",
    "dataset.groupby('class').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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": [
    "# Statistical Summary\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Plots\n",
    "Univariate plots to better understand each attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting each variable\n",
    "# box and whiskers plot\n",
    "dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False, figsize=(15,13))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(kind='hist', subplots=True, layout = (2,2), sharex=False, sharey=False, figsize=(15,13))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Plots\n",
    "Multivariate plots to better understand the relationships between attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(dataset, figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Algorithms\n",
    "_Describe the tools and techniques you will use necessary for a model to make a prediction_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Validation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 80-20 train-test-split\n",
    "from sklearn.model_selection import train_test_split\n",
    "array = dataset.values\n",
    "X = array[:, 0:4]\n",
    "y = array[:, 4]\n",
    "test = 0.2\n",
    "seed = 53\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test, random_state = seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = 'accuracy'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using the metric of ‘accuracy‘ to evaluate models. This is a ratio of the number of correctly predicted instances in divided by the total number of instances in the dataset multiplied by 100 to give a percentage (e.g. 95% accurate). We will be using the scoring variable when we run build and evaluate each model next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Developing Model\n",
    "We'll evaluate these 6 algorithms:\n",
    "\n",
    "- Logistic Regression (LR)\n",
    "- Linear Discriminate Analysis (LDA)\n",
    "- K-Nearest Neighbours (KNN)\n",
    "- Classification and Regression Trees (CRT)\n",
    "- Guassian Naive Bayes (GNN)\n",
    "- Support Vector Machine (SVM)\n",
    "\n",
    "This is a good mixture of simple linear (LR and LDA), nonlinear (KNN, CRT, NB and SVM) algorithms. We reset the random number seed before each run to ensure that the evaluation of each algorithm is performed using exactly the same data splits. It ensures the results are directly comparable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CRT', DecisionTreeClassifier()))\n",
    "models.append(('GNN', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "models.append(('RF', RandomForestClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR :  0.95 0.055277079839256664\n",
      "LDA :  0.9833333333333332 0.03333333333333335\n",
      "KNN :  0.9583333333333333 0.04166666666666669\n",
      "CRT :  0.9666666666666666 0.055277079839256664\n",
      "GNN :  0.95 0.055277079839256664\n",
      "SVM :  0.975 0.03818813079129868\n",
      "RF :  0.975 0.03818813079129868\n"
     ]
    }
   ],
   "source": [
    "# Now evaluating each model\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "# looping models in the list\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=seed, shuffle=True)\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv = kfold, scoring = scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print(name, ': ', cv_results.mean(), cv_results.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimentionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ETC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>ETC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.461489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.329650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.147077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>0.061783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ETC\n",
       "petal_length  0.461489\n",
       "petal_width   0.329650\n",
       "sepal_length  0.147077\n",
       "sepal_width   0.061783"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing model for feature importance\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# passing the model\n",
    "model = ExtraTreesClassifier(random_state = 53)\n",
    "\n",
    "X = dataset.iloc[:, 0:4]\n",
    "y = dataset.iloc[:, -1:]\n",
    "\n",
    "# training the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# extracting feature importance from model and making a dataframe of it in descending order\n",
    "ETC_feature_importances = pd.DataFrame(model.feature_importances_, index = X.columns, columns=['ETC']).sort_values('ETC', ascending=False)\n",
    "\n",
    "# removing traces of this model\n",
    "model = None\n",
    "\n",
    "# results\n",
    "ETC_feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>RFC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.500073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.414914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.076769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>0.008244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RFC\n",
       "petal_width   0.500073\n",
       "petal_length  0.414914\n",
       "sepal_length  0.076769\n",
       "sepal_width   0.008244"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# passing the model\n",
    "model = RandomForestClassifier(random_state = 53)\n",
    "\n",
    "# training the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# extracting feature importance from model and making a dataframe of it in descending order\n",
    "RFC_feature_importances = pd.DataFrame(model.feature_importances_, index = X.columns, columns=['RFC']).sort_values('RFC', ascending=False)\n",
    "\n",
    "# removing traces of this model\n",
    "model = None\n",
    "\n",
    "# show top 10 features\n",
    "RFC_feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ADBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>ADB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ADB\n",
       "petal_length  0.54\n",
       "petal_width   0.46\n",
       "sepal_length  0.00\n",
       "sepal_width   0.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing model for feature importance\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# passing the model\n",
    "model = AdaBoostClassifier(random_state = 53)\n",
    "\n",
    "model.fit(X, y)\n",
    "\n",
    "# extracting feature importance from model and making a dataframe of it in descending order\n",
    "ADB_feature_importances = pd.DataFrame(model.feature_importances_, index = X.columns, columns=['ADB']).sort_values('ADB', ascending=False)\n",
    "\n",
    "# removing traces of this model\n",
    "model = None\n",
    "\n",
    "ADB_feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>GBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.799085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.183235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>0.013661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.004019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GBC\n",
       "petal_width   0.799085\n",
       "petal_length  0.183235\n",
       "sepal_width   0.013661\n",
       "sepal_length  0.004019"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing model for feature importance\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# passing the model\n",
    "model = GradientBoostingClassifier(random_state = 53)\n",
    "\n",
    "# training the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# extracting feature importance from model and making a dataframe of it in descending order\n",
    "GBC_feature_importances = pd.DataFrame(model.feature_importances_, index = X.columns, columns=['GBC']).sort_values('GBC', ascending=False)\n",
    "\n",
    "# removing traces of this model\n",
    "model = None\n",
    "\n",
    "# show top 10 features\n",
    "GBC_feature_importances.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select K Best Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['petal_length', 'petal_width'], dtype='object')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "kbest = SelectKBest(k = 3).fit(X,y)\n",
    "mask = kbest.get_support()\n",
    "new_features = X.columns[mask]\n",
    "\n",
    "new_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9333333333333333\n",
      "[[10  0  0]\n",
      " [ 0  9  2]\n",
      " [ 0  0  9]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        10\n",
      "Iris-versicolor       1.00      0.82      0.90        11\n",
      " Iris-virginica       0.82      1.00      0.90         9\n",
      "\n",
      "      micro avg       0.93      0.93      0.93        30\n",
      "      macro avg       0.94      0.94      0.93        30\n",
      "   weighted avg       0.95      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "lda =  DecisionTreeClassifier()\n",
    "\n",
    "lda.fit(X_train, y_train)\n",
    "\n",
    "predict = lda.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_test, predict))\n",
    "print(confusion_matrix(y_test, predict))\n",
    "print(classification_report(y_test, predict))\n",
    "\n",
    "lda = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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