88 lines
1.7 KiB
Plaintext
88 lines
1.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "kFEl9T0h7KS1"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "3viYCvJl7U3I"
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},
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"outputs": [],
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"source": [
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"df = pd.read_csv('datasets-2/sms_spam_perceptron.csv')\n",
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"x = df.iloc[:,[1,2]].values\n",
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"y=df.iloc[:, 0].values\n",
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"y= np.where(y=='spam',-1,1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "RKMJB7gJ7rpj",
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"outputId": "3d701a48-151c-40d3-82cd-d8e216c40808"
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},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LinearRegression\n",
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"linear_regression = LinearRegression()\n",
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"linear_regression.fit(x,y)\n",
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"print(linear_regression.score(x,y))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"name": "Linear Regression for spam detection.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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