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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,353 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 55, | ||
| "id": "7f9de4c2", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "import matplotlib.pyplot as plt" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 56, | ||
| "id": "4d0833a3", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "data = pd.read_csv(\"knn_data.csv\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 57, | ||
| "id": "06f78e9e", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<div>\n", | ||
| "<style scoped>\n", | ||
| " .dataframe tbody tr th:only-of-type {\n", | ||
| " vertical-align: middle;\n", | ||
| " }\n", | ||
| "\n", | ||
| " .dataframe tbody tr th {\n", | ||
| " vertical-align: top;\n", | ||
| " }\n", | ||
| "\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>X</th>\n", | ||
| " <th>Y</th>\n", | ||
| " <th>Colour</th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <th>0</th>\n", | ||
| " <td>2</td>\n", | ||
| " <td>4</td>\n", | ||
| " <td>Orange</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>1</th>\n", | ||
| " <td>4</td>\n", | ||
| " <td>4</td>\n", | ||
| " <td>Blue</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>2</th>\n", | ||
| " <td>4</td>\n", | ||
| " <td>6</td>\n", | ||
| " <td>Orange</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>3</th>\n", | ||
| " <td>4</td>\n", | ||
| " <td>2</td>\n", | ||
| " <td>Orange</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>4</th>\n", | ||
| " <td>6</td>\n", | ||
| " <td>2</td>\n", | ||
| " <td>Blue</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>5</th>\n", | ||
| " <td>6</td>\n", | ||
| " <td>4</td>\n", | ||
| " <td>Orange</td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "</div>" | ||
| ], | ||
| "text/plain": [ | ||
| " X Y Colour\n", | ||
| "0 2 4 Orange\n", | ||
| "1 4 4 Blue\n", | ||
| "2 4 6 Orange\n", | ||
| "3 4 2 Orange\n", | ||
| "4 6 2 Blue\n", | ||
| "5 6 4 Orange" | ||
| ] | ||
| }, | ||
| "execution_count": 57, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 58, | ||
| "id": "9c2db9cb", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "X = data.iloc[:,:-1].values\n", | ||
| "Y = data.iloc[:,-1].values" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 60, | ||
| "id": "c982e784", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "array(['Orange', 'Blue', 'Orange', 'Orange', 'Blue', 'Orange'],\n", | ||
| " dtype=object)" | ||
| ] | ||
| }, | ||
| "execution_count": 60, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "Y\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 61, | ||
| "id": "96a3eea7", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "c = ['b' if x==\"Blue\" else 'orange' for x in Y]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 62, | ||
| "id": "28801e31", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "['orange', 'b', 'orange', 'orange', 'b', 'orange']" | ||
| ] | ||
| }, | ||
| "execution_count": 62, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "c" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 63, | ||
| "id": "368e6588", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "<matplotlib.collections.PathCollection at 0x1b7681aa3a0>" | ||
| ] | ||
| }, | ||
| "execution_count": 63, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| }, | ||
| { | ||
| "data": { | ||
| "image/png": 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\n", | ||
| "text/plain": [ | ||
| "<Figure size 432x288 with 1 Axes>" | ||
| ] | ||
| }, | ||
| "metadata": { | ||
| "needs_background": "light" | ||
| }, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "plt.scatter(X[:,:-1],X[:,-1],color=c)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 71, | ||
| "id": "692bbef9", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from sklearn.neighbors import KNeighborsClassifier,NearestNeighbors\n", | ||
| "knn = KNeighborsClassifier(n_neighbors=3)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 65, | ||
| "id": "3d3fd02e", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "KNeighborsClassifier(n_neighbors=3)" | ||
| ] | ||
| }, | ||
| "execution_count": 65, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "knn.fit(X,Y)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 66, | ||
| "id": "f36fcfbe", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "prediction = knn.predict([[6,6]])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 73, | ||
| "id": "c6fdc38b", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "['Orange']\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "print(prediction)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 74, | ||
| "id": "ee5897e9", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "knn_2 = KNeighborsClassifier(weights='distance')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 75, | ||
| "id": "1f8b161a", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "KNeighborsClassifier(weights='distance')" | ||
| ] | ||
| }, | ||
| "execution_count": 75, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "knn_2.fit(X,Y)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 76, | ||
| "id": "83b3f16b", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "['Orange']\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "prediction2 = knn_2.predict([[6,6]])\n", | ||
| "print(prediction2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "c2d5f10f", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python [conda env:root] *", | ||
| "language": "python", | ||
| "name": "conda-root-py" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.12" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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