{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as pt\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "train = pd.read_csv(\"./fashion-mnist_train.csv\")\n", "data = pd.read_csv(\"./fashion-mnist_train.csv\").values" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[2 0 0 ... 0 0 0]\n", " [9 0 0 ... 0 0 0]\n", " [6 0 0 ... 0 0 0]\n", " ...\n", " [8 0 0 ... 0 0 0]\n", " [8 0 0 ... 0 0 0]\n", " [7 0 0 ... 0 0 0]]\n" ] } ], "source": [ "print(data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "clf = DecisionTreeClassifier()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
DecisionTreeClassifier()
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": [ "DecisionTreeClassifier()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xtrain = data[0:21000,1:]\n", "train_label = data[0:21000,0]\n", "clf.fit(xtrain,train_label)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "xtest = data[21000:,1:]\n", "actual_label = data[21000:,0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = xtest[8]\n", "d.shape=(28,28)\n", "pt.imshow(255-d,cmap='gray')\n", "pt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1]\n" ] } ], "source": [ "print (clf.predict([xtest[8]]))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "p = clf.predict(xtest)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "16179\n", "77.04285714285714\n" ] } ], "source": [ "count_accuracy = 0\n", "for i in range(0,21000):\n", " count_accuracy+=1 if p[i]==actual_label[i] else 0\n", "print (count_accuracy)\n", "print ((count_accuracy/21000)*100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 2 }