{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "14 K Nearest Neighbors" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('Classified Data', index_col=0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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	WTT	PTI	EQW	SBI	LQE	QWG	FDJ	PJF	HQE	NXJ	TARGET CLASS
0	0.913917	1.162073	0.567946	0.755464	0.780862	0.352608	0.759697	0.643798	0.879422	1.231409	1
1	0.635632	1.003722	0.535342	0.825645	0.924109	0.648450	0.675334	1.013546	0.621552	1.492702	0
2	0.721360	1.201493	0.921990	0.855595	1.526629	0.720781	1.626351	1.154483	0.957877	1.285597	0
3	1.234204	1.386726	0.653046	0.825624	1.142504	0.875128	1.409708	1.380003	1.522692	1.153093	1
4	1.279491	0.949750	0.627280	0.668976	1.232537	0.703727	1.115596	0.646691	1.463812	1.419167	1

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" ], "text/plain": [ " WTT PTI EQW SBI LQE QWG FDJ \n", "0 0.913917 1.162073 0.567946 0.755464 0.780862 0.352608 0.759697 \\\n", "1 0.635632 1.003722 0.535342 0.825645 0.924109 0.648450 0.675334 \n", "2 0.721360 1.201493 0.921990 0.855595 1.526629 0.720781 1.626351 \n", "3 1.234204 1.386726 0.653046 0.825624 1.142504 0.875128 1.409708 \n", "4 1.279491 0.949750 0.627280 0.668976 1.232537 0.703727 1.115596 \n", "\n", " PJF HQE NXJ TARGET CLASS \n", "0 0.643798 0.879422 1.231409 1 \n", "1 1.013546 0.621552 1.492702 0 \n", "2 1.154483 0.957877 1.285597 0 \n", "3 1.380003 1.522692 1.153093 1 \n", "4 0.646691 1.463812 1.419167 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# in job interviews is common to have a dataset with random columns name and a given target column\n", "# the aim is to find the best columns to use in the model and the relationship between them\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

StandardScaler()

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": [ "StandardScaler()" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler.fit(\n", " # make the scaler fit the data without the target column\n", " df.drop('TARGET CLASS', axis=1)\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# perform a transformation that will standardize the data (mean = 0, variance = 1)\n", "# in order to make the data have the same scale\n", "scaled_features = scaler.transform(df.drop('TARGET CLASS', axis=1))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	WTT	PTI	EQW	SBI	LQE	QWG	FDJ	PJF	HQE	NXJ
0	-0.123542	0.185907	-0.913431	0.319629	-1.033637	-2.308375	-0.798951	-1.482368	-0.949719	-0.643314
1	-1.084836	-0.430348	-1.025313	0.625388	-0.444847	-1.152706	-1.129797	-0.202240	-1.828051	0.636759
2	-0.788702	0.339318	0.301511	0.755873	2.031693	-0.870156	2.599818	0.285707	-0.682494	-0.377850
3	0.982841	1.060193	-0.621399	0.625299	0.452820	-0.267220	1.750208	1.066491	1.241325	-1.026987
4	1.139275	-0.640392	-0.709819	-0.057175	0.822886	-0.936773	0.596782	-1.472352	1.040772	0.276510

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" ], "text/plain": [ " WTT PTI EQW SBI LQE QWG FDJ \n", "0 -0.123542 0.185907 -0.913431 0.319629 -1.033637 -2.308375 -0.798951 \\\n", "1 -1.084836 -0.430348 -1.025313 0.625388 -0.444847 -1.152706 -1.129797 \n", "2 -0.788702 0.339318 0.301511 0.755873 2.031693 -0.870156 2.599818 \n", "3 0.982841 1.060193 -0.621399 0.625299 0.452820 -0.267220 1.750208 \n", "4 1.139275 -0.640392 -0.709819 -0.057175 0.822886 -0.936773 0.596782 \n", "\n", " PJF HQE NXJ \n", "0 -1.482368 -0.949719 -0.643314 \n", "1 -0.202240 -1.828051 0.636759 \n", "2 0.285707 -0.682494 -0.377850 \n", "3 1.066491 1.241325 -1.026987 \n", "4 -1.472352 1.040772 0.276510 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create a new dataframe with the scaled features and the same columns name\n", "# scaling the data is important because the KNN classifier predicts the class of a given test observation\n", "df_feat = pd.DataFrame(\n", " scaled_features,\n", " columns=df.columns[:-1]\n", ")\n", "df_feat.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X = df_feat\n", "y = df['TARGET CLASS']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "knn = KNeighborsClassifier(\n", " n_neighbors=1\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "knn.fit(X_train, y_train)\n", "\n", "p = knn.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[134 20]\n", " [ 8 138]]\n", " precision recall f1-score support\n", "\n", " 0 0.94 0.87 0.91 154\n", " 1 0.87 0.95 0.91 146\n", "\n", " accuracy 0.91 300\n", " macro avg 0.91 0.91 0.91 300\n", "weighted avg 0.91 0.91 0.91 300\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report, confusion_matrix\n", "\n", "print(confusion_matrix(y_test, p))\n", "print(classification_report(y_test, p))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# in order to find the best k value it's possible to loop on a range of values\n", "# and use the loop index to train and predict the model with different k values.\n", "# by comparing the error rate it's possible to find the best k value\n", "error_rate = []\n", "\n", "for i in range(1,40):\n", " knn = KNeighborsClassifier(n_neighbors=i)\n", " knn.fit(X_train, y_train)\n", " \n", " p_i = knn.predict(X_test)\n", " error_rate.append(\n", " # average error rate, it's the mean of predicted values that are different from the real values\n", " np.mean(p_i != y_test)\n", " )" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Error Rate')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,6))\n", "plt.plot(range(1,40), error_rate, color='blue', linestyle='dashed', marker='o', markerfacecolor='red', markersize=10)\n", "plt.title('Error Rate vs K Value')\n", "plt.xlabel('K')\n", "plt.ylabel('Error Rate')\n", "\n", "# it's possible to see that it starts with a high error rate and then it decreases\n", "# a suitable choice for k is 17 because it's the first value that has a low error rate.\n", "# increasing the k value will not improve the model because it will start to overfit the data\n", "# and it will take more time to train the model" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[141 13]\n", " [ 6 140]]\n", " precision recall f1-score support\n", "\n", " 0 0.96 0.92 0.94 154\n", " 1 0.92 0.96 0.94 146\n", "\n", " accuracy 0.94 300\n", " macro avg 0.94 0.94 0.94 300\n", "weighted avg 0.94 0.94 0.94 300\n", "\n" ] } ], "source": [ "model = KNeighborsClassifier(\n", " # best key value found in previous step\n", " n_neighbors=17\n", ")\n", "\n", "model.fit(X_train, y_train)\n", "\n", "p = model.predict(X_test)\n", "\n", "print(confusion_matrix(y_test, p))\n", "print(classification_report(y_test, p))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.1" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }