TASK4_Amarjeet Kaur.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK-4 To Explore Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\SADDAL\\Documents\\Iris.csv\", header=0)"
   ]
  },
  {
   "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",
       "        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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Information of Datasets"
   ]
  },
  {
   "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 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\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>43.445368</td>\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>1.000000</td>\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>38.250000</td>\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>75.500000</td>\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>112.750000</td>\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>150.000000</td>\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": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since there are no NULL values we move to next part "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot between Length and width of petal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x15b1a1af0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"PetalLengthCm\",y=\"PetalWidthCm\",data=df,kind=\"scatter\",xlim=(0, 8), ylim=(0, 4),color=\"g\", height=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sepal length variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b3135b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(x=\"SepalLengthCm\",data=df,color=\"m\",palette=\"Set3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy variable creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing categorical variables into numerical variables\n",
    "df1 = pd.get_dummies(df, columns = [\"Species\"])"
   ]
  },
  {
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species_Iris-setosa</th>\n",
       "      <th>Species_Iris-versicolor</th>\n",
       "      <th>Species_Iris-virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0   1            5.1           3.5            1.4           0.2   \n",
       "1   2            4.9           3.0            1.4           0.2   \n",
       "2   3            4.7           3.2            1.3           0.2   \n",
       "3   4            4.6           3.1            1.5           0.2   \n",
       "4   5            5.0           3.6            1.4           0.2   \n",
       "\n",
       "   Species_Iris-setosa  Species_Iris-versicolor  Species_Iris-virginica  \n",
       "0                    1                        0                       0  \n",
       "1                    1                        0                       0  \n",
       "2                    1                        0                       0  \n",
       "3                    1                        0                       0  \n",
       "4                    1                        0                       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X-Y Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing dependant variable\n",
    "X=df.drop([\"Id\",\"Species\"], axis=1)\n",
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0            5.1           3.5            1.4           0.2\n",
       "1            4.9           3.0            1.4           0.2\n",
       "2            4.7           3.2            1.3           0.2\n",
       "3            4.6           3.1            1.5           0.2\n",
       "4            5.0           3.6            1.4           0.2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df[\"Species\"]\n",
    "type(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test-Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing our model on real life data\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train dataset is used to fit the machine learning model\n",
    "#test dataset is used to evaluate the fit machine learning model(prediction)\n",
    "#random state is initialized to 0 so as to get the same dataset everytime\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "137            6.4           3.1            5.5           1.8\n",
       "84             5.4           3.0            4.5           1.5\n",
       "27             5.2           3.5            1.5           0.2\n",
       "127            6.1           3.0            4.9           1.8\n",
       "132            6.4           2.8            5.6           2.2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "114            5.8           2.8            5.1           2.4\n",
       "62             6.0           2.2            4.0           1.0\n",
       "33             5.5           4.2            1.4           0.2\n",
       "107            7.3           2.9            6.3           1.8\n",
       "7              5.0           3.4            1.5           0.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Classification Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "clftree = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#object to predict values of Y using our X train and expressed variable\n",
    "clftree.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict values using Trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting values\n",
    "Y_train_pred = clftree.predict(X_train)\n",
    "Y_test_pred = clftree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-virginica', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica', 'Iris-setosa',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predicted values\n",
    "Y_test_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39,  0,  0],\n",
       "       [ 0, 37,  0],\n",
       "       [ 0,  0, 44]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Y_train, Y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  0,  0],\n",
       "       [ 0, 13,  0],\n",
       "       [ 0,  0,  6]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Y_test, Y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test, Y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b941a00>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RPRExJyLmnAohK0k/3rRJM2bOVEdnpyZNmqTFS5boybVriy4rOxzn9Ab/73Ud+vVBvX3K2ZKkM9o69Mb+VwquauIMDQ+PextLROyJiOcqtw9I6pVU1zhXtR7tXtsXRsTzlTc7aHuRpNWSPljPG5bV0NCQbl++XPevX6/W1lY9vHq1+rbU/IsLVXCcJ8aup7+rGX90pdzSokMHX9OO7z9RdEkTppaTXCP/+q7oiYieUZ7XKWm2pKfrqcljdbNtT5M0GBF7R3nskoj4frU3mG4zAIdT3qP33VV0CU3hw8tuOeFfyuN1xyc+Me7MufOxx6q+X2UiwEZJfxcRj9ZT05g92ojoH+OxqiELABOtkdO2bE+S9E1JD9YbshIXLADITKNOhtm2pK9L6o2Ik/qThgsWAGSlgRcsXCLpM5I+avv5ynZVPTXRowWQlUZdghsR/6kxZlfVgqAFkBWWSQSAxAhaAEgsSrh6F0ELICv0aAEgMYIWABIbHBoquoTjELQAslLGZTcJWgBZYegAABIbZtYBAKRFjxYAEuNkGAAkNkSPFgDSYowWABJjjBYAEqNHCwCJNWo92kYiaAFkZYBZBwCQFkMHAJAYQwcAkBhBCwCJEbQAkBhjtACQ2JuHDhVdwnEIWgBZGaRHCwBpDTGPFgDSGnjzzYa1ZfsKSfdIapW0KiK+VE87BC2ArAw0aIzWdquklZIWSOqXtMn22ojYUmtbBC2ArLx+4ECjmporaVtE/FySbK+R1CWpfEG7M8Kp36PRbHdHRE/RdeSMY5xesx7jWjLHdrek7hG7ekYcs3ZJu0Y81i/pI/XU1FLPi5pAd/Wn4CRxjNPjGFcRET0RMWfENvIX02iBXdditwQtAIyuX1LHiPvTJO2upyGCFgBGt0nSTNszbL9N0hJJa+tpiJNho2u6ca0CcIzT4xifhIgYtL1c0nodnt61OiJerKctRwm/XwcAcsLQAQAkRtACQGIE7Qi2r7D9U9vbbN9adD05sr3a9su2Xyi6llzZ7rD9Xdu9tl+0fVPRNTU7xmgrKpfb/UwjLreTtLSey+1wYrYvlXRQ0v0RMavoenJku01SW0Q8Z/sMSc9K+jif5eLQoz3qyOV2EXFI0m8ut0MDRcRTkl4puo6cRcSeiHiucvuApF4dvsoJBSFojxrtcjs+nDil2e6UNFvS08VW0twI2qMadrkdUAa2J0v6pqSbI+JXRdfTzAjaoxp2uR1QNNuTdDhkH4yIR4uup9kRtEc17HI7oEi2Lenrknoj4q6i6wFBe0REDEr6zeV2vZIervdyO5yY7Yck/UDS+233276u6JoydImkz0j6qO3nK9tVRRfVzJjeBQCJ0aMFgMQIWgBIjKAFgMQIWgBIjKAFgMQIWgBIjKAFgMT+H4tMwdyjWKyMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(Y_test, Y_test_pred),annot= True,cmap=\"pink\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#created dot file converted into image and used the image to plot graph\n",
    "dot_data = tree.export_graphviz(clftree,feature_names=X_train.columns, out_file = None, filled= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can now feed any new/test data to this classifer and it would be able to predict the right class accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}