{ "cells": [ { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# importing required libraries \n", "import numpy as np \n", "import pandas as pd \n", "import matplotlib.pyplot as plt " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0monthdayFFMCDMCDCISItempRHwind...monthfebmonthjanmonthjulmonthjunmonthmarmonthmaymonthnovmonthoctmonthsepsize_category
01marfri86.226.294.35.18.2516.7...000010000small
12octtue90.635.4669.16.718.0330.9...000000010small
23octsat90.643.7686.96.714.6331.3...000000010small
34marfri91.733.377.59.08.3974.0...000010000small
45marsun89.351.3102.29.611.4991.8...000010000small
..................................................................
512513augsun81.656.7665.61.927.8322.7...000000000large
513514augsun81.656.7665.61.921.9715.8...000000000large
514515augsun81.656.7665.61.921.2706.7...000000000large
515516augsat94.4146.0614.711.325.6424.0...000000000small
516517novtue79.53.0106.71.111.8314.5...000000100small
\n", "

517 rows × 32 columns

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" ], "text/plain": [ " Unnamed: 0 month day FFMC DMC DC ISI temp RH wind ... \\\n", "0 1 mar fri 86.2 26.2 94.3 5.1 8.2 51 6.7 ... \n", "1 2 oct tue 90.6 35.4 669.1 6.7 18.0 33 0.9 ... \n", "2 3 oct sat 90.6 43.7 686.9 6.7 14.6 33 1.3 ... \n", "3 4 mar fri 91.7 33.3 77.5 9.0 8.3 97 4.0 ... \n", "4 5 mar sun 89.3 51.3 102.2 9.6 11.4 99 1.8 ... \n", ".. ... ... ... ... ... ... ... ... .. ... ... \n", "512 513 aug sun 81.6 56.7 665.6 1.9 27.8 32 2.7 ... \n", "513 514 aug sun 81.6 56.7 665.6 1.9 21.9 71 5.8 ... \n", "514 515 aug sun 81.6 56.7 665.6 1.9 21.2 70 6.7 ... \n", "515 516 aug sat 94.4 146.0 614.7 11.3 25.6 42 4.0 ... \n", "516 517 nov tue 79.5 3.0 106.7 1.1 11.8 31 4.5 ... \n", "\n", " monthfeb monthjan monthjul monthjun monthmar monthmay monthnov \\\n", "0 0 0 0 0 1 0 0 \n", "1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 1 0 0 \n", "4 0 0 0 0 1 0 0 \n", ".. ... ... ... ... ... ... ... \n", "512 0 0 0 0 0 0 0 \n", "513 0 0 0 0 0 0 0 \n", "514 0 0 0 0 0 0 0 \n", "515 0 0 0 0 0 0 0 \n", "516 0 0 0 0 0 0 1 \n", "\n", " monthoct monthsep size_category \n", "0 0 0 small \n", "1 1 0 small \n", "2 1 0 small \n", "3 0 0 small \n", "4 0 0 small \n", ".. ... ... ... \n", "512 0 0 large \n", "513 0 0 large \n", "514 0 0 large \n", "515 0 0 small \n", "516 0 0 small \n", "\n", "[517 rows x 32 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading csv file and extracting class column to y. \n", "forestfires = pd.read_csv(\"~/Downloads/Data Science/data set/forestfires.csv\")\n", "forestfires" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "#drop unnecessary columan\n", "forestfires.drop(['month','day','Unnamed: 0'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "#feture selection\n", "y = forestfires.size_category\n", "forestfires.drop(['size_category'], axis=1, inplace=True)\n", "X = forestfires" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "#convert category to int\n", "y.replace('small',0,regex=True, inplace = True)\n", "y.replace('large',1,regex=True, inplace = True)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "#Splitting Data\n", "\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=0.4,random_state=109)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "from sklearn import svm\n", "#create a classifier\n", "cls = svm.SVC(kernel=\"linear\")\n", "#train the model\n", "cls.fit(X_train,y_train)\n", "#predict the response\n", "pred = cls.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "acuracy: 1.0\n", "precision: 1.0\n", "recall 1.0\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 150\n", " 1 1.00 1.00 1.00 57\n", "\n", " accuracy 1.00 207\n", " macro avg 1.00 1.00 1.00 207\n", "weighted avg 1.00 1.00 1.00 207\n", "\n" ] } ], "source": [ "#Evaluating the Model\n", "from sklearn import metrics\n", "#accuracy\n", "print(\"acuracy:\", metrics.accuracy_score(y_test,y_pred=pred))\n", "#precision score\n", "print(\"precision:\", metrics.precision_score(y_test,y_pred=pred))\n", "#recall score\n", "print(\"recall\" , metrics.recall_score(y_test,y_pred=pred))\n", "print(metrics.classification_report(y_test, y_pred=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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 4 }