{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1abbf44e", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "52024597", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('iris.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "53f85ec3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
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24.73.21.30.2setosa
34.63.11.50.2setosa
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "70f12208", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3virginica
1495.93.05.11.8virginica
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "145 6.7 3.0 5.2 2.3 virginica\n", "146 6.3 2.5 5.0 1.9 virginica\n", "147 6.5 3.0 5.2 2.0 virginica\n", "148 6.2 3.4 5.4 2.3 virginica\n", "149 5.9 3.0 5.1 1.8 virginica" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "code", "execution_count": 5, "id": "d18c5f2d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 150 entries, 0 to 149\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 sepal_length 150 non-null float64\n", " 1 sepal_width 150 non-null float64\n", " 2 petal_length 150 non-null float64\n", " 3 petal_width 150 non-null float64\n", " 4 species 150 non-null object \n", "dtypes: float64(4), object(1)\n", "memory usage: 6.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "98ac41d7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count150.000000150.000000150.000000150.000000
mean5.8433333.0573333.7580001.199333
std0.8280660.4358661.7652980.762238
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "count 150.000000 150.000000 150.000000 150.000000\n", "mean 5.843333 3.057333 3.758000 1.199333\n", "std 0.828066 0.435866 1.765298 0.762238\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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 7, "id": "d9210c7a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(150, 5)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 8, "id": "88829673", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sepal_length 0\n", "sepal_width 0\n", "petal_length 0\n", "petal_width 0\n", "species 0\n", "dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 12, "id": "a10e5824", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " sepal_length sepal_width petal_length petal_width\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\n", ".. ... ... ... ...\n", "145 6.7 3.0 5.2 2.3\n", "146 6.3 2.5 5.0 1.9\n", "147 6.5 3.0 5.2 2.0\n", "148 6.2 3.4 5.4 2.3\n", "149 5.9 3.0 5.1 1.8\n", "\n", "[150 rows x 4 columns]\n", " species\n", "0 setosa\n", "1 setosa\n", "2 setosa\n", "3 setosa\n", "4 setosa\n", ".. ...\n", "145 virginica\n", "146 virginica\n", "147 virginica\n", "148 virginica\n", "149 virginica\n", "\n", "[150 rows x 1 columns]\n", "(150, 4)\n", "(150, 1)\n" ] } ], "source": [ "X = df.drop(['species'], axis=1)\n", "y = df.drop(['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], axis=1)\n", "print(X)\n", "print(y)\n", "print(X.shape)\n", "print(y.shape)" ] }, { "cell_type": "code", "execution_count": 13, "id": "d6b1618f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(120, 4)\n", "(30, 4)\n", "(120, 1)\n", "(30, 1)\n" ] } ], "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, shuffle=True)\n", "print(X_train.shape)\n", "print(X_test.shape)\n", "print(y_train.shape)\n", "print(y_test.shape)" ] }, { "cell_type": "code", "execution_count": 14, "id": "37eed9ea", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\HP\\.conda\\anakonda\\Lib\\site-packages\\sklearn\\utils\\validation.py:1184: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "data": { "text/html": [ "
GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "GaussianNB()" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "model = GaussianNB()\n", "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 15, "id": "14bdfbf4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9666666666666667" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = model.predict(X_test)\n", "model.score(X_test,y_test)" ] }, { "cell_type": "code", "execution_count": 16, "id": "85b8d699", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9666666666666667\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n", "print(accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 17, "id": "a79e5b8f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix:\n", "[[12 0 0]\n", " [ 0 9 1]\n", " [ 0 0 8]]\n" ] } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "disp = ConfusionMatrixDisplay(confusion_matrix = cm)\n", "print(\"Confusion matrix:\")\n", "print(cm)" ] }, { "cell_type": "code", "execution_count": 18, "id": "0724221a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "disp.plot()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "id": "9f362cb9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TP: 12\n", "FP: 0\n", "FN: 0\n", "TN: 9\n" ] } ], "source": [ "def get_confusion_matrix_values(y_true, y_pred):\n", " cm = confusion_matrix(y_true, y_pred)\n", " return(cm[0][0], cm[0][1], cm[1][0], cm[1][1])\n", "\n", "TP, FP, FN, TN = get_confusion_matrix_values(y_test, y_pred)\n", "print(\"TP: \", TP)\n", "print(\"FP: \", FP)\n", "print(\"FN: \", FN)\n", "print(\"TN: \", TN)" ] }, { "cell_type": "code", "execution_count": 20, "id": "57f531ad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TP: 12\n", "FP: 0\n", "FN: 0\n", "TN: 9\n" ] } ], "source": [ "\n", "TP, FP, FN, TN = get_confusion_matrix_values(y_test, y_pred)\n", "print(\"TP: \", TP)\n", "print(\"FP: \", FP)\n", "print(\"FN: \", FN)\n", "print(\"TN: \", TN)" ] }, { "cell_type": "code", "execution_count": null, "id": "2addd4e9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }