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Classification-mini-project
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Classification-mini-project
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{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "Untitled0.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyOnI5CRxHhMpBY0LDAUDGDC",
"include_colab_link": true
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{
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"<a href=\"https://colab.research.google.com/github/sathya050801/iris_classification/blob/master/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "B0UZQsH8HwyJ",
"colab_type": "text"
},
"source": [
"# Iris Data Classification\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BLZ9vxJbITAu",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np\n",
"import pandas as pd"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6yvloFRUIaWs",
"colab_type": "code",
"colab": {}
},
"source": [
"dataset=pd.read_csv(\"Iris.csv\")"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
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"height": 197
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"outputId": "6c022845-bdaa-4e14-cf5a-d657077f6456"
},
"source": [
"dataset.head()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"<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"
]
},
"metadata": {
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},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
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"outputId": "53e17b81-a171-410d-d62f-ecb419393a58"
},
"source": [
"dataset[\"Species\"].value_counts()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Iris-versicolor 50\n",
"Iris-setosa 50\n",
"Iris-virginica 50\n",
"Name: Species, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8BfNB6-UI1uJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "77efa5e7-9b86-435a-b428-c2789894691a"
},
"source": [
"dataset.describe()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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"\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"
]
},
"metadata": {
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"execution_count": 8
}
]
},
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"metadata": {
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},
"source": [
"x=dataset.iloc[:,1:5].values\n",
"y=dataset.iloc[:,-1].values"
],
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
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"x"
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"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[5.1, 3.5, 1.4, 0.2],\n",
" [4.9, 3. , 1.4, 0.2],\n",
" [4.7, 3.2, 1.3, 0.2],\n",
" [4.6, 3.1, 1.5, 0.2],\n",
" [5. , 3.6, 1.4, 0.2],\n",
" [5.4, 3.9, 1.7, 0.4],\n",
" [4.6, 3.4, 1.4, 0.3],\n",
" [5. , 3.4, 1.5, 0.2],\n",
" [4.4, 2.9, 1.4, 0.2],\n",
" [4.9, 3.1, 1.5, 0.1],\n",
" [5.4, 3.7, 1.5, 0.2],\n",
" [4.8, 3.4, 1.6, 0.2],\n",
" [4.8, 3. , 1.4, 0.1],\n",
" [4.3, 3. , 1.1, 0.1],\n",
" [5.8, 4. , 1.2, 0.2],\n",
" [5.7, 4.4, 1.5, 0.4],\n",
" [5.4, 3.9, 1.3, 0.4],\n",
" [5.1, 3.5, 1.4, 0.3],\n",
" [5.7, 3.8, 1.7, 0.3],\n",
" [5.1, 3.8, 1.5, 0.3],\n",
" [5.4, 3.4, 1.7, 0.2],\n",
" [5.1, 3.7, 1.5, 0.4],\n",
" [4.6, 3.6, 1. , 0.2],\n",
" [5.1, 3.3, 1.7, 0.5],\n",
" [4.8, 3.4, 1.9, 0.2],\n",
" [5. , 3. , 1.6, 0.2],\n",
" [5. , 3.4, 1.6, 0.4],\n",
" [5.2, 3.5, 1.5, 0.2],\n",
" [5.2, 3.4, 1.4, 0.2],\n",
" [4.7, 3.2, 1.6, 0.2],\n",
" [4.8, 3.1, 1.6, 0.2],\n",
" [5.4, 3.4, 1.5, 0.4],\n",
" [5.2, 4.1, 1.5, 0.1],\n",
" [5.5, 4.2, 1.4, 0.2],\n",
" [4.9, 3.1, 1.5, 0.1],\n",
" [5. , 3.2, 1.2, 0.2],\n",
" [5.5, 3.5, 1.3, 0.2],\n",
" [4.9, 3.1, 1.5, 0.1],\n",
" [4.4, 3. , 1.3, 0.2],\n",
" [5.1, 3.4, 1.5, 0.2],\n",
" [5. , 3.5, 1.3, 0.3],\n",
" [4.5, 2.3, 1.3, 0.3],\n",
" [4.4, 3.2, 1.3, 0.2],\n",
" [5. , 3.5, 1.6, 0.6],\n",
" [5.1, 3.8, 1.9, 0.4],\n",
" [4.8, 3. , 1.4, 0.3],\n",
" [5.1, 3.8, 1.6, 0.2],\n",
" [4.6, 3.2, 1.4, 0.2],\n",
" [5.3, 3.7, 1.5, 0.2],\n",
" [5. , 3.3, 1.4, 0.2],\n",
" [7. , 3.2, 4.7, 1.4],\n",
" [6.4, 3.2, 4.5, 1.5],\n",
" [6.9, 3.1, 4.9, 1.5],\n",
" [5.5, 2.3, 4. , 1.3],\n",
" [6.5, 2.8, 4.6, 1.5],\n",
" [5.7, 2.8, 4.5, 1.3],\n",
" [6.3, 3.3, 4.7, 1.6],\n",
" [4.9, 2.4, 3.3, 1. ],\n",
" [6.6, 2.9, 4.6, 1.3],\n",
" [5.2, 2.7, 3.9, 1.4],\n",
" [5. , 2. , 3.5, 1. ],\n",
" [5.9, 3. , 4.2, 1.5],\n",
" [6. , 2.2, 4. , 1. ],\n",
" [6.1, 2.9, 4.7, 1.4],\n",
" [5.6, 2.9, 3.6, 1.3],\n",
" [6.7, 3.1, 4.4, 1.4],\n",
" [5.6, 3. , 4.5, 1.5],\n",
" [5.8, 2.7, 4.1, 1. ],\n",
" [6.2, 2.2, 4.5, 1.5],\n",
" [5.6, 2.5, 3.9, 1.1],\n",
" [5.9, 3.2, 4.8, 1.8],\n",
" [6.1, 2.8, 4. , 1.3],\n",
" [6.3, 2.5, 4.9, 1.5],\n",
" [6.1, 2.8, 4.7, 1.2],\n",
" [6.4, 2.9, 4.3, 1.3],\n",
" [6.6, 3. , 4.4, 1.4],\n",
" [6.8, 2.8, 4.8, 1.4],\n",
" [6.7, 3. , 5. , 1.7],\n",
" [6. , 2.9, 4.5, 1.5],\n",
" [5.7, 2.6, 3.5, 1. ],\n",
" [5.5, 2.4, 3.8, 1.1],\n",
" [5.5, 2.4, 3.7, 1. ],\n",
" [5.8, 2.7, 3.9, 1.2],\n",
" [6. , 2.7, 5.1, 1.6],\n",
" [5.4, 3. , 4.5, 1.5],\n",
" [6. , 3.4, 4.5, 1.6],\n",
" [6.7, 3.1, 4.7, 1.5],\n",
" [6.3, 2.3, 4.4, 1.3],\n",
" [5.6, 3. , 4.1, 1.3],\n",
" [5.5, 2.5, 4. , 1.3],\n",
" [5.5, 2.6, 4.4, 1.2],\n",
" [6.1, 3. , 4.6, 1.4],\n",
" [5.8, 2.6, 4. , 1.2],\n",
" [5. , 2.3, 3.3, 1. ],\n",
" [5.6, 2.7, 4.2, 1.3],\n",
" [5.7, 3. , 4.2, 1.2],\n",
" [5.7, 2.9, 4.2, 1.3],\n",
" [6.2, 2.9, 4.3, 1.3],\n",
" [5.1, 2.5, 3. , 1.1],\n",
" [5.7, 2.8, 4.1, 1.3],\n",
" [6.3, 3.3, 6. , 2.5],\n",
" [5.8, 2.7, 5.1, 1.9],\n",
" [7.1, 3. , 5.9, 2.1],\n",
" [6.3, 2.9, 5.6, 1.8],\n",
" [6.5, 3. , 5.8, 2.2],\n",
" [7.6, 3. , 6.6, 2.1],\n",
" [4.9, 2.5, 4.5, 1.7],\n",
" [7.3, 2.9, 6.3, 1.8],\n",
" [6.7, 2.5, 5.8, 1.8],\n",
" [7.2, 3.6, 6.1, 2.5],\n",
" [6.5, 3.2, 5.1, 2. ],\n",
" [6.4, 2.7, 5.3, 1.9],\n",
" [6.8, 3. , 5.5, 2.1],\n",
" [5.7, 2.5, 5. , 2. ],\n",
" [5.8, 2.8, 5.1, 2.4],\n",
" [6.4, 3.2, 5.3, 2.3],\n",
" [6.5, 3. , 5.5, 1.8],\n",
" [7.7, 3.8, 6.7, 2.2],\n",
" [7.7, 2.6, 6.9, 2.3],\n",
" [6. , 2.2, 5. , 1.5],\n",
" [6.9, 3.2, 5.7, 2.3],\n",
" [5.6, 2.8, 4.9, 2. ],\n",
" [7.7, 2.8, 6.7, 2. ],\n",
" [6.3, 2.7, 4.9, 1.8],\n",
" [6.7, 3.3, 5.7, 2.1],\n",
" [7.2, 3.2, 6. , 1.8],\n",
" [6.2, 2.8, 4.8, 1.8],\n",
" [6.1, 3. , 4.9, 1.8],\n",
" [6.4, 2.8, 5.6, 2.1],\n",
" [7.2, 3. , 5.8, 1.6],\n",
" [7.4, 2.8, 6.1, 1.9],\n",
" [7.9, 3.8, 6.4, 2. ],\n",
" [6.4, 2.8, 5.6, 2.2],\n",
" [6.3, 2.8, 5.1, 1.5],\n",
" [6.1, 2.6, 5.6, 1.4],\n",
" [7.7, 3. , 6.1, 2.3],\n",
" [6.3, 3.4, 5.6, 2.4],\n",
" [6.4, 3.1, 5.5, 1.8],\n",
" [6. , 3. , 4.8, 1.8],\n",
" [6.9, 3.1, 5.4, 2.1],\n",
" [6.7, 3.1, 5.6, 2.4],\n",
" [6.9, 3.1, 5.1, 2.3],\n",
" [5.8, 2.7, 5.1, 1.9],\n",
" [6.8, 3.2, 5.9, 2.3],\n",
" [6.7, 3.3, 5.7, 2.5],\n",
" [6.7, 3. , 5.2, 2.3],\n",
" [6.3, 2.5, 5. , 1.9],\n",
" [6.5, 3. , 5.2, 2. ],\n",
" [6.2, 3.4, 5.4, 2.3],\n",
" [5.9, 3. , 5.1, 1.8]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SMAtOqCCJY-s",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 799
},
"outputId": "6e3c627e-3c89-41c2-cdaf-99a95088f744"
},
"source": [
"y"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
" 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
" 'Iris-virginica', 'Iris-virginica'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "v1cHRkCPJZ9q",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.preprocessing import LabelBinarizer\n",
"lb = LabelBinarizer()\n",
"y=lb.fit_transform(y)\n"
],
"execution_count": 26,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VfdctUdPKMJA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "09b4809a-c024-43e5-df58-0432567d5b1d"
},
"source": [
"y"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[1, 0, 0],\n",
" [1, 0, 0],\n",
" [1, 0, 0],\n",
" [1, 0, 0],\n",
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" [0, 1, 0],\n",
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" [0, 1, 0],\n",
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" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 1]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7S7TSqpwKSdT",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=0)"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bIqE8TPSLtrG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"outputId": "ed1fdfa7-02e6-406e-bd92-23ace06fc82e"
},
"source": [
"X_train[0:5,:]"
],
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[6.4, 3.1, 5.5, 1.8],\n",
" [5.4, 3. , 4.5, 1.5],\n",
" [5.2, 3.5, 1.5, 0.2],\n",
" [6.1, 3. , 4.9, 1.8],\n",
" [6.4, 2.8, 5.6, 2.2]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_Q0aKFZwL3_T",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
},
"outputId": "10e55f5a-c272-459b-b5cc-eaaf147a8ea7"
},
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"classifier = RandomForestClassifier(n_estimators=100)\n",
"classifier.fit(X_train,y_train)\n"
],
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
" criterion='gini', max_depth=None, max_features='auto',\n",
" max_leaf_nodes=None, max_samples=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
" n_jobs=None, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zeuvqf7rMmLJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 527
},
"outputId": "e7616ec8-fcd0-40fe-f944-8509a6a6bfd3"
},
"source": [
"y_pred=classifier.predict(X_test)\n",
"y_pred"
],
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0, 0, 1],\n",
" [0, 1, 0],\n",
" [1, 0, 0],\n",
" [0, 0, 1],\n",
" [1, 0, 0],\n",
" [0, 0, 1],\n",
" [1, 0, 0],\n",
" [0, 1, 0],\n",
" [0, 1, 0],\n",
" [0, 1, 0],\n",
" [0, 0, 1],\n",
" [0, 1, 0],\n",
" [0, 1, 0],\n",
" [0, 1, 0],\n",
" [0, 1, 0],\n",
" [1, 0, 0],\n",
" [0, 1, 0],\n",
" [0, 1, 0],\n",
" [1, 0, 0],\n",
" [1, 0, 0],\n",
" [0, 0, 1],\n",
" [0, 1, 0],\n",
" [1, 0, 0],\n",
" [1, 0, 0],\n",
" [0, 0, 1],\n",
" [1, 0, 0],\n",
" [1, 0, 0],\n",
" [0, 1, 0],\n",
" [0, 1, 0],\n",
" [1, 0, 0]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "O6vI0P7kM8MV",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"outputId": "c7c33189-1dd4-4366-e66c-b143239a8394"
},
"source": [
"from sklearn.metrics import confusion_matrix,accuracy_score\n",
"cm= confusion_matrix(np.argmax(y_pred,axis=1),np.argmax(y_test,axis=1))\n",
"cm"
],
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[11, 0, 0],\n",
" [ 0, 13, 0],\n",
" [ 0, 0, 6]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
}
]
},
{
"cell_type": "code",