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Davud Topalovic 2022-12-22 11:10:56 +01:00
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Decision Tree Classification in Python.pdf Normal file
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# Decision-Tree-Algorithm
Here we explain the concept and math behind Decision Tree Algorithm, code it completely from scratch using only two libraries: numpy and pandas and implement it.
In this repository you can find following files:
1. pdf file: Decision Tree Classification in Python.pdf which is a seminary paper (under the work) that explains the concept and math behind Decision Tree Algorithm
2. Jupyter notebook file: Decision_Tree_Classifier.ipynb that consists the code of our Decision Tree Algorithm as well as it's two implementations(two differents datasets)
3. First dataset: iris_data.csv
4. Second dataset: iphone_purchase_records.csv

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decision tree classification.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import tools"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>type</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",
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" <td>0</td>\n",
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" <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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
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" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>4.4</td>\n",
" <td>2.9</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</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",
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" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width type\n",
"0 5.1 3.5 1.4 0.2 0\n",
"1 4.9 3.0 1.4 0.2 0\n",
"2 4.7 3.2 1.3 0.2 0\n",
"3 4.6 3.1 1.5 0.2 0\n",
"4 5.0 3.6 1.4 0.2 0\n",
"5 5.4 3.9 1.7 0.4 0\n",
"6 4.6 3.4 1.4 0.3 0\n",
"7 5.0 3.4 1.5 0.2 0\n",
"8 4.4 2.9 1.4 0.2 0\n",
"9 4.9 3.1 1.5 0.1 0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'type']\n",
"data = pd.read_csv(\"iris.csv\", skiprows=1, header=None, names=col_names)\n",
"data.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Node class"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class Node():\n",
" def __init__(self, feature_index=None, threshold=None, left=None, right=None, info_gain=None, value=None):\n",
" ''' constructor ''' \n",
" \n",
" # for decision node\n",
" self.feature_index = feature_index\n",
" self.threshold = threshold\n",
" self.left = left\n",
" self.right = right\n",
" self.info_gain = info_gain\n",
" \n",
" # for leaf node\n",
" self.value = value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tree class"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class DecisionTreeClassifier():\n",
" def __init__(self, min_samples_split=2, max_depth=2):\n",
" ''' constructor '''\n",
" \n",
" # initialize the root of the tree \n",
" self.root = None\n",
" \n",
" # stopping conditions\n",
" self.min_samples_split = min_samples_split\n",
" self.max_depth = max_depth\n",
" \n",
" def build_tree(self, dataset, curr_depth=0):\n",
" ''' recursive function to build the tree ''' \n",
" \n",
" X, Y = dataset[:,:-1], dataset[:,-1]\n",
" num_samples, num_features = np.shape(X)\n",
" \n",
" # split until stopping conditions are met\n",
" if num_samples>=self.min_samples_split and curr_depth<=self.max_depth:\n",
" # find the best split\n",
" best_split = self.get_best_split(dataset, num_samples, num_features)\n",
" # check if information gain is positive\n",
" if best_split[\"info_gain\"]>0:\n",
" # recur left\n",
" left_subtree = self.build_tree(best_split[\"dataset_left\"], curr_depth+1)\n",
" # recur right\n",
" right_subtree = self.build_tree(best_split[\"dataset_right\"], curr_depth+1)\n",
" # return decision node\n",
" return Node(best_split[\"feature_index\"], best_split[\"threshold\"], \n",
" left_subtree, right_subtree, best_split[\"info_gain\"])\n",
" \n",
" # compute leaf node\n",
" leaf_value = self.calculate_leaf_value(Y)\n",
" # return leaf node\n",
" return Node(value=leaf_value)\n",
" \n",
" def get_best_split(self, dataset, num_samples, num_features):\n",
" ''' function to find the best split '''\n",
" \n",
" # dictionary to store the best split\n",
" best_split = {}\n",
" max_info_gain = -float(\"inf\")\n",
" \n",
" # loop over all the features\n",
" for feature_index in range(num_features):\n",
" feature_values = dataset[:, feature_index]\n",
" possible_thresholds = np.unique(feature_values)\n",
" # loop over all the feature values present in the data\n",
" for threshold in possible_thresholds:\n",
" # get current split\n",
" dataset_left, dataset_right = self.split(dataset, feature_index, threshold)\n",
" # check if childs are not null\n",
" if len(dataset_left)>0 and len(dataset_right)>0:\n",
" y, left_y, right_y = dataset[:, -1], dataset_left[:, -1], dataset_right[:, -1]\n",
" # compute information gain\n",
" curr_info_gain = self.information_gain(y, left_y, right_y, \"gini\")\n",
" # update the best split if needed\n",
" if curr_info_gain>max_info_gain:\n",
" best_split[\"feature_index\"] = feature_index\n",
" best_split[\"threshold\"] = threshold\n",
" best_split[\"dataset_left\"] = dataset_left\n",
" best_split[\"dataset_right\"] = dataset_right\n",
" best_split[\"info_gain\"] = curr_info_gain\n",
" max_info_gain = curr_info_gain\n",
" \n",
" # return best split\n",
" return best_split\n",
" \n",
" def split(self, dataset, feature_index, threshold):\n",
" ''' function to split the data '''\n",
" \n",
" dataset_left = np.array([row for row in dataset if row[feature_index]<=threshold])\n",
" dataset_right = np.array([row for row in dataset if row[feature_index]>threshold])\n",
" return dataset_left, dataset_right\n",
" \n",
" def information_gain(self, parent, l_child, r_child, mode=\"entropy\"):\n",
" ''' function to compute information gain '''\n",
" \n",
" weight_l = len(l_child) / len(parent)\n",
" weight_r = len(r_child) / len(parent)\n",
" if mode==\"gini\":\n",
" gain = self.gini_index(parent) - (weight_l*self.gini_index(l_child) + weight_r*self.gini_index(r_child))\n",
" else:\n",
" gain = self.entropy(parent) - (weight_l*self.entropy(l_child) + weight_r*self.entropy(r_child))\n",
" return gain\n",
" \n",
" def entropy(self, y):\n",
" ''' function to compute entropy '''\n",
" \n",
" class_labels = np.unique(y)\n",
" entropy = 0\n",
" for cls in class_labels:\n",
" p_cls = len(y[y == cls]) / len(y)\n",
" entropy += -p_cls * np.log2(p_cls)\n",
" return entropy\n",
" \n",
" def gini_index(self, y):\n",
" ''' function to compute gini index '''\n",
" \n",
" class_labels = np.unique(y)\n",
" gini = 0\n",
" for cls in class_labels:\n",
" p_cls = len(y[y == cls]) / len(y)\n",
" gini += p_cls**2\n",
" return 1 - gini\n",
" \n",
" def calculate_leaf_value(self, Y):\n",
" ''' function to compute leaf node '''\n",
" \n",
" Y = list(Y)\n",
" return max(Y, key=Y.count)\n",
" \n",
" def print_tree(self, tree=None, indent=\" \"):\n",
" ''' function to print the tree '''\n",
" \n",
" if not tree:\n",
" tree = self.root\n",
"\n",
" if tree.value is not None:\n",
" print(tree.value)\n",
"\n",
" else:\n",
" print(\"X_\"+str(tree.feature_index), \"<=\", tree.threshold, \"?\", tree.info_gain)\n",
" print(\"%sleft:\" % (indent), end=\"\")\n",
" self.print_tree(tree.left, indent + indent)\n",
" print(\"%sright:\" % (indent), end=\"\")\n",
" self.print_tree(tree.right, indent + indent)\n",
" \n",
" def fit(self, X, Y):\n",
" ''' function to train the tree '''\n",
" \n",
" dataset = np.concatenate((X, Y), axis=1)\n",
" self.root = self.build_tree(dataset)\n",
" \n",
" def predict(self, X):\n",
" ''' function to predict new dataset '''\n",
" \n",
" preditions = [self.make_prediction(x, self.root) for x in X]\n",
" return preditions\n",
" \n",
" def make_prediction(self, x, tree):\n",
" ''' function to predict a single data point '''\n",
" \n",
" if tree.value!=None: return tree.value\n",
" feature_val = x[tree.feature_index]\n",
" if feature_val<=tree.threshold:\n",
" return self.make_prediction(x, tree.left)\n",
" else:\n",
" return self.make_prediction(x, tree.right)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train-Test split"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"X = data.iloc[:, :-1].values\n",
"Y = data.iloc[:, -1].values.reshape(-1,1)\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=.2, random_state=41)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit the model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_2 <= 1.9 ? 0.33741385372714494\n",
" left:0.0\n",
" right:X_3 <= 1.5 ? 0.427106638180289\n",
" left:X_2 <= 4.9 ? 0.05124653739612173\n",
" left:1.0\n",
" right:2.0\n",
" right:X_2 <= 5.0 ? 0.019631171921475288\n",
" left:X_1 <= 2.8 ? 0.20833333333333334\n",
" left:2.0\n",
" right:1.0\n",
" right:2.0\n"
]
}
],
"source": [
"classifier = DecisionTreeClassifier(min_samples_split=3, max_depth=3)\n",
"classifier.fit(X_train,Y_train)\n",
"classifier.print_tree()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test the model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9333333333333333"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_pred = classifier.predict(X_test) \n",
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(Y_test, Y_pred)"
]
}
],
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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iphone_purchase_records.csv Normal file
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Gender,Age,Salary,Purchase Iphone
Male,19,19000,0
Male,35,20000,0
Female,26,43000,0
Female,27,57000,0
Male,19,76000,0
Male,27,58000,0
Female,27,84000,0
Female,32,150000,1
Male,25,33000,0
Female,35,65000,0
Female,26,80000,0
Female,26,52000,0
Male,20,86000,0
Male,32,18000,0
Male,18,82000,0
Male,29,80000,0
Male,47,25000,1
Male,45,26000,1
Male,46,28000,1
Female,48,29000,1
Male,45,22000,1
Female,47,49000,1
Male,48,41000,1
Female,45,22000,1
Male,46,23000,1
Male,47,20000,1
Male,49,28000,1
Female,47,30000,1
Male,29,43000,0
Male,31,18000,0
Male,31,74000,0
Female,27,137000,1
Female,21,16000,0
Female,28,44000,0
Male,27,90000,0
Male,35,27000,0
Female,33,28000,0
Male,30,49000,0
Female,26,72000,0
Female,27,31000,0
Female,27,17000,0
Female,33,51000,0
Male,35,108000,0
Male,30,15000,0
Female,28,84000,0
Male,23,20000,0
Male,25,79000,0
Female,27,54000,0
Male,30,135000,1
Female,31,89000,0
Female,24,32000,0
Female,18,44000,0
Female,29,83000,0
Female,35,23000,0
Female,27,58000,0
Female,24,55000,0
Female,23,48000,0
Male,28,79000,0
Male,22,18000,0
Female,32,117000,0
Male,27,20000,0
Male,25,87000,0
Female,23,66000,0
Male,32,120000,1
Female,59,83000,0
Male,24,58000,0
Male,24,19000,0
Female,23,82000,0
Female,22,63000,0
Female,31,68000,0
Male,25,80000,0
Female,24,27000,0
Female,20,23000,0
Female,33,113000,0
Male,32,18000,0
Male,34,112000,1
Male,18,52000,0
Female,22,27000,0
Female,28,87000,0
Female,26,17000,0
Male,30,80000,0
Male,39,42000,0
Male,20,49000,0
Male,35,88000,0
Female,30,62000,0
Female,31,118000,1
Male,24,55000,0
Female,28,85000,0
Male,26,81000,0
Male,35,50000,0
Male,22,81000,0
Female,30,116000,0
Male,26,15000,0
Female,29,28000,0
Female,29,83000,0
Female,35,44000,0
Female,35,25000,0
Male,28,123000,1
Male,35,73000,0
Female,28,37000,0
Male,27,88000,0
Male,28,59000,0
Female,32,86000,0
Female,33,149000,1
Female,19,21000,0
Male,21,72000,0
Female,26,35000,0
Male,27,89000,0
Male,26,86000,0
Female,38,80000,0
Female,39,71000,0
Female,37,71000,0
Male,38,61000,0
Male,37,55000,0
Male,42,80000,0
Male,40,57000,0
Male,35,75000,0
Male,36,52000,0
Male,40,59000,0
Male,41,59000,0
Female,36,75000,0
Male,37,72000,0
Female,40,75000,0
Male,35,53000,0
Female,41,51000,0
Female,39,61000,0
Male,42,65000,0
Male,26,32000,0
Male,30,17000,0
Female,26,84000,0
Male,31,58000,0
Male,33,31000,0
Male,30,87000,0
Female,21,68000,0
Female,28,55000,0
Male,23,63000,0
Female,20,82000,0
Male,30,107000,1
Female,28,59000,0
Male,19,25000,0
Male,19,85000,0
Female,18,68000,0
Male,35,59000,0
Male,30,89000,0
Female,34,25000,0
Female,24,89000,0
Female,27,96000,1
Female,41,30000,0
Male,29,61000,0
Male,20,74000,0
Female,26,15000,0
Male,41,45000,0
Male,31,76000,0
Female,36,50000,0
Male,40,47000,0
Female,31,15000,0
Male,46,59000,0
Male,29,75000,0
Male,26,30000,0
Female,32,135000,1
Male,32,100000,1
Male,25,90000,0
Female,37,33000,0
Male,35,38000,0
Female,33,69000,0
Female,18,86000,0
Female,22,55000,0
Female,35,71000,0
Male,29,148000,1
Female,29,47000,0
Male,21,88000,0
Male,34,115000,0
Female,26,118000,0
Female,34,43000,0
Female,34,72000,0
Female,23,28000,0
Female,35,47000,0
Male,25,22000,0
Male,24,23000,0
Female,31,34000,0
Male,26,16000,0
Female,31,71000,0
Female,32,117000,1
Male,33,43000,0
Female,33,60000,0
Male,31,66000,0
Female,20,82000,0
Female,33,41000,0
Male,35,72000,0
Male,28,32000,0
Male,24,84000,0
Female,19,26000,0
Male,29,43000,0
Male,19,70000,0
Male,28,89000,0
Male,34,43000,0
Female,30,79000,0
Female,20,36000,0
Male,26,80000,0
Male,35,22000,0
Male,35,39000,0
Male,49,74000,0
Female,39,134000,1
Female,41,71000,0
Female,58,101000,1
Female,47,47000,0
Female,55,130000,1
Female,52,114000,0
Female,40,142000,1
Female,46,22000,0
Female,48,96000,1
Male,52,150000,1
Female,59,42000,0
Male,35,58000,0
Male,47,43000,0
Female,60,108000,1
Male,49,65000,0
Male,40,78000,0
Female,46,96000,0
Male,59,143000,1
Female,41,80000,0
Male,35,91000,1
Male,37,144000,1
Male,60,102000,1
Female,35,60000,0
Male,37,53000,0
Female,36,126000,1
Male,56,133000,1
Female,40,72000,0
Female,42,80000,1
Female,35,147000,1
Male,39,42000,0
Male,40,107000,1
Male,49,86000,1
Female,38,112000,0
Male,46,79000,1
Male,40,57000,0
Female,37,80000,0
Female,46,82000,0
Female,53,143000,1
Male,42,149000,1
Male,38,59000,0
Female,50,88000,1
Female,56,104000,1
Female,41,72000,0
Female,51,146000,1
Female,35,50000,0
Female,57,122000,1
Male,41,52000,0
Female,35,97000,1
Female,44,39000,0
Male,37,52000,0
Female,48,134000,1
Female,37,146000,1
Female,50,44000,0
Female,52,90000,1
Female,41,72000,0
Male,40,57000,0
Female,58,95000,1
Female,45,131000,1
Female,35,77000,0
Male,36,144000,1
Female,55,125000,1
Female,35,72000,0
Male,48,90000,1
Female,42,108000,1
Male,40,75000,0
Male,37,74000,0
Female,47,144000,1
Male,40,61000,0
Female,43,133000,0
Female,59,76000,1
Male,60,42000,1
Male,39,106000,1
Female,57,26000,1
Male,57,74000,1
Male,38,71000,0
Male,49,88000,1
Female,52,38000,1
Female,50,36000,1
Female,59,88000,1
Male,35,61000,0
Male,37,70000,1
Female,52,21000,1
Male,48,141000,0
Female,37,93000,1
Female,37,62000,0
Female,48,138000,1
Male,41,79000,0
Female,37,78000,1
Male,39,134000,1
Male,49,89000,1
Male,55,39000,1
Male,37,77000,0
Female,35,57000,0
Female,36,63000,0
Male,42,73000,1
Female,43,112000,1
Male,45,79000,0
Male,46,117000,1
Female,58,38000,1
Male,48,74000,1
Female,37,137000,1
Male,37,79000,1
Female,40,60000,0
Male,42,54000,0
Female,51,134000,0
Female,47,113000,1
Male,36,125000,1
Female,38,50000,0
Female,42,70000,0
Male,39,96000,1
Female,38,50000,0
Female,49,141000,1
Female,39,79000,0
Female,39,75000,1
Female,54,104000,1
Male,35,55000,0
Male,45,32000,1
Male,36,60000,0
Female,52,138000,1
Female,53,82000,1
Male,41,52000,0
Female,48,30000,1
Female,48,131000,1
Female,41,60000,0
Male,41,72000,0
Female,42,75000,0
Male,36,118000,1
Female,47,107000,1
Male,38,51000,0
Female,48,119000,1
Male,42,65000,0
Male,40,65000,0
Male,57,60000,1
Female,36,54000,0
Male,58,144000,1
Male,35,79000,0
Female,38,55000,0
Male,39,122000,1
Female,53,104000,1
Male,35,75000,0
Female,38,65000,0
Female,47,51000,1
Male,47,105000,1
Female,41,63000,0
Male,53,72000,1
Female,54,108000,1
Male,39,77000,0
Male,38,61000,0
Female,38,113000,1
Male,37,75000,0
Female,42,90000,1
Female,37,57000,0
Male,36,99000,1
Male,60,34000,1
Male,54,70000,1
Female,41,72000,0
Male,40,71000,1
Male,42,54000,0
Male,43,129000,1
Female,53,34000,1
Female,47,50000,1
Female,42,79000,0
Male,42,104000,1
Female,59,29000,1
Female,58,47000,1
Male,46,88000,1
Male,38,71000,0
Female,54,26000,1
Female,60,46000,1
Male,60,83000,1
Female,39,73000,0
Male,59,130000,1
Female,37,80000,0
Female,46,32000,1
Female,46,74000,0
Female,42,53000,0
Male,41,87000,1
Female,58,23000,1
Male,42,64000,0
Male,48,33000,1
Female,44,139000,1
Male,49,28000,1
Female,57,33000,1
Male,56,60000,1
Female,49,39000,1
Male,39,71000,0
Male,47,34000,1
Female,48,35000,1
Male,48,33000,1
Male,47,23000,1
Female,45,45000,1
Male,60,42000,1
Female,39,59000,0
Female,46,41000,1
Male,51,23000,1
Female,50,20000,1
Male,36,33000,0
Female,49,36000,1
1 Gender Age Salary Purchase Iphone
2 Male 19 19000 0
3 Male 35 20000 0
4 Female 26 43000 0
5 Female 27 57000 0
6 Male 19 76000 0
7 Male 27 58000 0
8 Female 27 84000 0
9 Female 32 150000 1
10 Male 25 33000 0
11 Female 35 65000 0
12 Female 26 80000 0
13 Female 26 52000 0
14 Male 20 86000 0
15 Male 32 18000 0
16 Male 18 82000 0
17 Male 29 80000 0
18 Male 47 25000 1
19 Male 45 26000 1
20 Male 46 28000 1
21 Female 48 29000 1
22 Male 45 22000 1
23 Female 47 49000 1
24 Male 48 41000 1
25 Female 45 22000 1
26 Male 46 23000 1
27 Male 47 20000 1
28 Male 49 28000 1
29 Female 47 30000 1
30 Male 29 43000 0
31 Male 31 18000 0
32 Male 31 74000 0
33 Female 27 137000 1
34 Female 21 16000 0
35 Female 28 44000 0
36 Male 27 90000 0
37 Male 35 27000 0
38 Female 33 28000 0
39 Male 30 49000 0
40 Female 26 72000 0
41 Female 27 31000 0
42 Female 27 17000 0
43 Female 33 51000 0
44 Male 35 108000 0
45 Male 30 15000 0
46 Female 28 84000 0
47 Male 23 20000 0
48 Male 25 79000 0
49 Female 27 54000 0
50 Male 30 135000 1
51 Female 31 89000 0
52 Female 24 32000 0
53 Female 18 44000 0
54 Female 29 83000 0
55 Female 35 23000 0
56 Female 27 58000 0
57 Female 24 55000 0
58 Female 23 48000 0
59 Male 28 79000 0
60 Male 22 18000 0
61 Female 32 117000 0
62 Male 27 20000 0
63 Male 25 87000 0
64 Female 23 66000 0
65 Male 32 120000 1
66 Female 59 83000 0
67 Male 24 58000 0
68 Male 24 19000 0
69 Female 23 82000 0
70 Female 22 63000 0
71 Female 31 68000 0
72 Male 25 80000 0
73 Female 24 27000 0
74 Female 20 23000 0
75 Female 33 113000 0
76 Male 32 18000 0
77 Male 34 112000 1
78 Male 18 52000 0
79 Female 22 27000 0
80 Female 28 87000 0
81 Female 26 17000 0
82 Male 30 80000 0
83 Male 39 42000 0
84 Male 20 49000 0
85 Male 35 88000 0
86 Female 30 62000 0
87 Female 31 118000 1
88 Male 24 55000 0
89 Female 28 85000 0
90 Male 26 81000 0
91 Male 35 50000 0
92 Male 22 81000 0
93 Female 30 116000 0
94 Male 26 15000 0
95 Female 29 28000 0
96 Female 29 83000 0
97 Female 35 44000 0
98 Female 35 25000 0
99 Male 28 123000 1
100 Male 35 73000 0
101 Female 28 37000 0
102 Male 27 88000 0
103 Male 28 59000 0
104 Female 32 86000 0
105 Female 33 149000 1
106 Female 19 21000 0
107 Male 21 72000 0
108 Female 26 35000 0
109 Male 27 89000 0
110 Male 26 86000 0
111 Female 38 80000 0
112 Female 39 71000 0
113 Female 37 71000 0
114 Male 38 61000 0
115 Male 37 55000 0
116 Male 42 80000 0
117 Male 40 57000 0
118 Male 35 75000 0
119 Male 36 52000 0
120 Male 40 59000 0
121 Male 41 59000 0
122 Female 36 75000 0
123 Male 37 72000 0
124 Female 40 75000 0
125 Male 35 53000 0
126 Female 41 51000 0
127 Female 39 61000 0
128 Male 42 65000 0
129 Male 26 32000 0
130 Male 30 17000 0
131 Female 26 84000 0
132 Male 31 58000 0
133 Male 33 31000 0
134 Male 30 87000 0
135 Female 21 68000 0
136 Female 28 55000 0
137 Male 23 63000 0
138 Female 20 82000 0
139 Male 30 107000 1
140 Female 28 59000 0
141 Male 19 25000 0
142 Male 19 85000 0
143 Female 18 68000 0
144 Male 35 59000 0
145 Male 30 89000 0
146 Female 34 25000 0
147 Female 24 89000 0
148 Female 27 96000 1
149 Female 41 30000 0
150 Male 29 61000 0
151 Male 20 74000 0
152 Female 26 15000 0
153 Male 41 45000 0
154 Male 31 76000 0
155 Female 36 50000 0
156 Male 40 47000 0
157 Female 31 15000 0
158 Male 46 59000 0
159 Male 29 75000 0
160 Male 26 30000 0
161 Female 32 135000 1
162 Male 32 100000 1
163 Male 25 90000 0
164 Female 37 33000 0
165 Male 35 38000 0
166 Female 33 69000 0
167 Female 18 86000 0
168 Female 22 55000 0
169 Female 35 71000 0
170 Male 29 148000 1
171 Female 29 47000 0
172 Male 21 88000 0
173 Male 34 115000 0
174 Female 26 118000 0
175 Female 34 43000 0
176 Female 34 72000 0
177 Female 23 28000 0
178 Female 35 47000 0
179 Male 25 22000 0
180 Male 24 23000 0
181 Female 31 34000 0
182 Male 26 16000 0
183 Female 31 71000 0
184 Female 32 117000 1
185 Male 33 43000 0
186 Female 33 60000 0
187 Male 31 66000 0
188 Female 20 82000 0
189 Female 33 41000 0
190 Male 35 72000 0
191 Male 28 32000 0
192 Male 24 84000 0
193 Female 19 26000 0
194 Male 29 43000 0
195 Male 19 70000 0
196 Male 28 89000 0
197 Male 34 43000 0
198 Female 30 79000 0
199 Female 20 36000 0
200 Male 26 80000 0
201 Male 35 22000 0
202 Male 35 39000 0
203 Male 49 74000 0
204 Female 39 134000 1
205 Female 41 71000 0
206 Female 58 101000 1
207 Female 47 47000 0
208 Female 55 130000 1
209 Female 52 114000 0
210 Female 40 142000 1
211 Female 46 22000 0
212 Female 48 96000 1
213 Male 52 150000 1
214 Female 59 42000 0
215 Male 35 58000 0
216 Male 47 43000 0
217 Female 60 108000 1
218 Male 49 65000 0
219 Male 40 78000 0
220 Female 46 96000 0
221 Male 59 143000 1
222 Female 41 80000 0
223 Male 35 91000 1
224 Male 37 144000 1
225 Male 60 102000 1
226 Female 35 60000 0
227 Male 37 53000 0
228 Female 36 126000 1
229 Male 56 133000 1
230 Female 40 72000 0
231 Female 42 80000 1
232 Female 35 147000 1
233 Male 39 42000 0
234 Male 40 107000 1
235 Male 49 86000 1
236 Female 38 112000 0
237 Male 46 79000 1
238 Male 40 57000 0
239 Female 37 80000 0
240 Female 46 82000 0
241 Female 53 143000 1
242 Male 42 149000 1
243 Male 38 59000 0
244 Female 50 88000 1
245 Female 56 104000 1
246 Female 41 72000 0
247 Female 51 146000 1
248 Female 35 50000 0
249 Female 57 122000 1
250 Male 41 52000 0
251 Female 35 97000 1
252 Female 44 39000 0
253 Male 37 52000 0
254 Female 48 134000 1
255 Female 37 146000 1
256 Female 50 44000 0
257 Female 52 90000 1
258 Female 41 72000 0
259 Male 40 57000 0
260 Female 58 95000 1
261 Female 45 131000 1
262 Female 35 77000 0
263 Male 36 144000 1
264 Female 55 125000 1
265 Female 35 72000 0
266 Male 48 90000 1
267 Female 42 108000 1
268 Male 40 75000 0
269 Male 37 74000 0
270 Female 47 144000 1
271 Male 40 61000 0
272 Female 43 133000 0
273 Female 59 76000 1
274 Male 60 42000 1
275 Male 39 106000 1
276 Female 57 26000 1
277 Male 57 74000 1
278 Male 38 71000 0
279 Male 49 88000 1
280 Female 52 38000 1
281 Female 50 36000 1
282 Female 59 88000 1
283 Male 35 61000 0
284 Male 37 70000 1
285 Female 52 21000 1
286 Male 48 141000 0
287 Female 37 93000 1
288 Female 37 62000 0
289 Female 48 138000 1
290 Male 41 79000 0
291 Female 37 78000 1
292 Male 39 134000 1
293 Male 49 89000 1
294 Male 55 39000 1
295 Male 37 77000 0
296 Female 35 57000 0
297 Female 36 63000 0
298 Male 42 73000 1
299 Female 43 112000 1
300 Male 45 79000 0
301 Male 46 117000 1
302 Female 58 38000 1
303 Male 48 74000 1
304 Female 37 137000 1
305 Male 37 79000 1
306 Female 40 60000 0
307 Male 42 54000 0
308 Female 51 134000 0
309 Female 47 113000 1
310 Male 36 125000 1
311 Female 38 50000 0
312 Female 42 70000 0
313 Male 39 96000 1
314 Female 38 50000 0
315 Female 49 141000 1
316 Female 39 79000 0
317 Female 39 75000 1
318 Female 54 104000 1
319 Male 35 55000 0
320 Male 45 32000 1
321 Male 36 60000 0
322 Female 52 138000 1
323 Female 53 82000 1
324 Male 41 52000 0
325 Female 48 30000 1
326 Female 48 131000 1
327 Female 41 60000 0
328 Male 41 72000 0
329 Female 42 75000 0
330 Male 36 118000 1
331 Female 47 107000 1
332 Male 38 51000 0
333 Female 48 119000 1
334 Male 42 65000 0
335 Male 40 65000 0
336 Male 57 60000 1
337 Female 36 54000 0
338 Male 58 144000 1
339 Male 35 79000 0
340 Female 38 55000 0
341 Male 39 122000 1
342 Female 53 104000 1
343 Male 35 75000 0
344 Female 38 65000 0
345 Female 47 51000 1
346 Male 47 105000 1
347 Female 41 63000 0
348 Male 53 72000 1
349 Female 54 108000 1
350 Male 39 77000 0
351 Male 38 61000 0
352 Female 38 113000 1
353 Male 37 75000 0
354 Female 42 90000 1
355 Female 37 57000 0
356 Male 36 99000 1
357 Male 60 34000 1
358 Male 54 70000 1
359 Female 41 72000 0
360 Male 40 71000 1
361 Male 42 54000 0
362 Male 43 129000 1
363 Female 53 34000 1
364 Female 47 50000 1
365 Female 42 79000 0
366 Male 42 104000 1
367 Female 59 29000 1
368 Female 58 47000 1
369 Male 46 88000 1
370 Male 38 71000 0
371 Female 54 26000 1
372 Female 60 46000 1
373 Male 60 83000 1
374 Female 39 73000 0
375 Male 59 130000 1
376 Female 37 80000 0
377 Female 46 32000 1
378 Female 46 74000 0
379 Female 42 53000 0
380 Male 41 87000 1
381 Female 58 23000 1
382 Male 42 64000 0
383 Male 48 33000 1
384 Female 44 139000 1
385 Male 49 28000 1
386 Female 57 33000 1
387 Male 56 60000 1
388 Female 49 39000 1
389 Male 39 71000 0
390 Male 47 34000 1
391 Female 48 35000 1
392 Male 48 33000 1
393 Male 47 23000 1
394 Female 45 45000 1
395 Male 60 42000 1
396 Female 39 59000 0
397 Female 46 41000 1
398 Male 51 23000 1
399 Female 50 20000 1
400 Male 36 33000 0
401 Female 49 36000 1

151
iris_data.csv Normal file
View File

@ -0,0 +1,151 @@
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
1 5.1 3.5 1.4 0.2 Iris-setosa
2 4.9 3.0 1.4 0.2 Iris-setosa
3 4.7 3.2 1.3 0.2 Iris-setosa
4 4.6 3.1 1.5 0.2 Iris-setosa
5 5.0 3.6 1.4 0.2 Iris-setosa
6 5.4 3.9 1.7 0.4 Iris-setosa
7 4.6 3.4 1.4 0.3 Iris-setosa
8 5.0 3.4 1.5 0.2 Iris-setosa
9 4.4 2.9 1.4 0.2 Iris-setosa
10 4.9 3.1 1.5 0.1 Iris-setosa
11 5.4 3.7 1.5 0.2 Iris-setosa
12 4.8 3.4 1.6 0.2 Iris-setosa
13 4.8 3.0 1.4 0.1 Iris-setosa
14 4.3 3.0 1.1 0.1 Iris-setosa
15 5.8 4.0 1.2 0.2 Iris-setosa
16 5.7 4.4 1.5 0.4 Iris-setosa
17 5.4 3.9 1.3 0.4 Iris-setosa
18 5.1 3.5 1.4 0.3 Iris-setosa
19 5.7 3.8 1.7 0.3 Iris-setosa
20 5.1 3.8 1.5 0.3 Iris-setosa
21 5.4 3.4 1.7 0.2 Iris-setosa
22 5.1 3.7 1.5 0.4 Iris-setosa
23 4.6 3.6 1.0 0.2 Iris-setosa
24 5.1 3.3 1.7 0.5 Iris-setosa
25 4.8 3.4 1.9 0.2 Iris-setosa
26 5.0 3.0 1.6 0.2 Iris-setosa
27 5.0 3.4 1.6 0.4 Iris-setosa
28 5.2 3.5 1.5 0.2 Iris-setosa
29 5.2 3.4 1.4 0.2 Iris-setosa
30 4.7 3.2 1.6 0.2 Iris-setosa
31 4.8 3.1 1.6 0.2 Iris-setosa
32 5.4 3.4 1.5 0.4 Iris-setosa
33 5.2 4.1 1.5 0.1 Iris-setosa
34 5.5 4.2 1.4 0.2 Iris-setosa
35 4.9 3.1 1.5 0.1 Iris-setosa
36 5.0 3.2 1.2 0.2 Iris-setosa
37 5.5 3.5 1.3 0.2 Iris-setosa
38 4.9 3.1 1.5 0.1 Iris-setosa
39 4.4 3.0 1.3 0.2 Iris-setosa
40 5.1 3.4 1.5 0.2 Iris-setosa
41 5.0 3.5 1.3 0.3 Iris-setosa
42 4.5 2.3 1.3 0.3 Iris-setosa
43 4.4 3.2 1.3 0.2 Iris-setosa
44 5.0 3.5 1.6 0.6 Iris-setosa
45 5.1 3.8 1.9 0.4 Iris-setosa
46 4.8 3.0 1.4 0.3 Iris-setosa
47 5.1 3.8 1.6 0.2 Iris-setosa
48 4.6 3.2 1.4 0.2 Iris-setosa
49 5.3 3.7 1.5 0.2 Iris-setosa
50 5.0 3.3 1.4 0.2 Iris-setosa
51 7.0 3.2 4.7 1.4 Iris-versicolor
52 6.4 3.2 4.5 1.5 Iris-versicolor
53 6.9 3.1 4.9 1.5 Iris-versicolor
54 5.5 2.3 4.0 1.3 Iris-versicolor
55 6.5 2.8 4.6 1.5 Iris-versicolor
56 5.7 2.8 4.5 1.3 Iris-versicolor
57 6.3 3.3 4.7 1.6 Iris-versicolor
58 4.9 2.4 3.3 1.0 Iris-versicolor
59 6.6 2.9 4.6 1.3 Iris-versicolor
60 5.2 2.7 3.9 1.4 Iris-versicolor
61 5.0 2.0 3.5 1.0 Iris-versicolor
62 5.9 3.0 4.2 1.5 Iris-versicolor
63 6.0 2.2 4.0 1.0 Iris-versicolor
64 6.1 2.9 4.7 1.4 Iris-versicolor
65 5.6 2.9 3.6 1.3 Iris-versicolor
66 6.7 3.1 4.4 1.4 Iris-versicolor
67 5.6 3.0 4.5 1.5 Iris-versicolor
68 5.8 2.7 4.1 1.0 Iris-versicolor
69 6.2 2.2 4.5 1.5 Iris-versicolor
70 5.6 2.5 3.9 1.1 Iris-versicolor
71 5.9 3.2 4.8 1.8 Iris-versicolor
72 6.1 2.8 4.0 1.3 Iris-versicolor
73 6.3 2.5 4.9 1.5 Iris-versicolor
74 6.1 2.8 4.7 1.2 Iris-versicolor
75 6.4 2.9 4.3 1.3 Iris-versicolor
76 6.6 3.0 4.4 1.4 Iris-versicolor
77 6.8 2.8 4.8 1.4 Iris-versicolor
78 6.7 3.0 5.0 1.7 Iris-versicolor
79 6.0 2.9 4.5 1.5 Iris-versicolor
80 5.7 2.6 3.5 1.0 Iris-versicolor
81 5.5 2.4 3.8 1.1 Iris-versicolor
82 5.5 2.4 3.7 1.0 Iris-versicolor
83 5.8 2.7 3.9 1.2 Iris-versicolor
84 6.0 2.7 5.1 1.6 Iris-versicolor
85 5.4 3.0 4.5 1.5 Iris-versicolor
86 6.0 3.4 4.5 1.6 Iris-versicolor
87 6.7 3.1 4.7 1.5 Iris-versicolor
88 6.3 2.3 4.4 1.3 Iris-versicolor
89 5.6 3.0 4.1 1.3 Iris-versicolor
90 5.5 2.5 4.0 1.3 Iris-versicolor
91 5.5 2.6 4.4 1.2 Iris-versicolor
92 6.1 3.0 4.6 1.4 Iris-versicolor
93 5.8 2.6 4.0 1.2 Iris-versicolor
94 5.0 2.3 3.3 1.0 Iris-versicolor
95 5.6 2.7 4.2 1.3 Iris-versicolor
96 5.7 3.0 4.2 1.2 Iris-versicolor
97 5.7 2.9 4.2 1.3 Iris-versicolor
98 6.2 2.9 4.3 1.3 Iris-versicolor
99 5.1 2.5 3.0 1.1 Iris-versicolor
100 5.7 2.8 4.1 1.3 Iris-versicolor
101 6.3 3.3 6.0 2.5 Iris-virginica
102 5.8 2.7 5.1 1.9 Iris-virginica
103 7.1 3.0 5.9 2.1 Iris-virginica
104 6.3 2.9 5.6 1.8 Iris-virginica
105 6.5 3.0 5.8 2.2 Iris-virginica
106 7.6 3.0 6.6 2.1 Iris-virginica
107 4.9 2.5 4.5 1.7 Iris-virginica
108 7.3 2.9 6.3 1.8 Iris-virginica
109 6.7 2.5 5.8 1.8 Iris-virginica
110 7.2 3.6 6.1 2.5 Iris-virginica
111 6.5 3.2 5.1 2.0 Iris-virginica
112 6.4 2.7 5.3 1.9 Iris-virginica
113 6.8 3.0 5.5 2.1 Iris-virginica
114 5.7 2.5 5.0 2.0 Iris-virginica
115 5.8 2.8 5.1 2.4 Iris-virginica
116 6.4 3.2 5.3 2.3 Iris-virginica
117 6.5 3.0 5.5 1.8 Iris-virginica
118 7.7 3.8 6.7 2.2 Iris-virginica
119 7.7 2.6 6.9 2.3 Iris-virginica
120 6.0 2.2 5.0 1.5 Iris-virginica
121 6.9 3.2 5.7 2.3 Iris-virginica
122 5.6 2.8 4.9 2.0 Iris-virginica
123 7.7 2.8 6.7 2.0 Iris-virginica
124 6.3 2.7 4.9 1.8 Iris-virginica
125 6.7 3.3 5.7 2.1 Iris-virginica
126 7.2 3.2 6.0 1.8 Iris-virginica
127 6.2 2.8 4.8 1.8 Iris-virginica
128 6.1 3.0 4.9 1.8 Iris-virginica
129 6.4 2.8 5.6 2.1 Iris-virginica
130 7.2 3.0 5.8 1.6 Iris-virginica
131 7.4 2.8 6.1 1.9 Iris-virginica
132 7.9 3.8 6.4 2.0 Iris-virginica
133 6.4 2.8 5.6 2.2 Iris-virginica
134 6.3 2.8 5.1 1.5 Iris-virginica
135 6.1 2.6 5.6 1.4 Iris-virginica
136 7.7 3.0 6.1 2.3 Iris-virginica
137 6.3 3.4 5.6 2.4 Iris-virginica
138 6.4 3.1 5.5 1.8 Iris-virginica
139 6.0 3.0 4.8 1.8 Iris-virginica
140 6.9 3.1 5.4 2.1 Iris-virginica
141 6.7 3.1 5.6 2.4 Iris-virginica
142 6.9 3.1 5.1 2.3 Iris-virginica
143 5.8 2.7 5.1 1.9 Iris-virginica
144 6.8 3.2 5.9 2.3 Iris-virginica
145 6.7 3.3 5.7 2.5 Iris-virginica
146 6.7 3.0 5.2 2.3 Iris-virginica
147 6.3 2.5 5.0 1.9 Iris-virginica
148 6.5 3.0 5.2 2.0 Iris-virginica
149 6.2 3.4 5.4 2.3 Iris-virginica
150 5.9 3.0 5.1 1.8 Iris-virginica