{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kFEl9T0h7KS1"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3viYCvJl7U3I"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets-2/sms_spam_perceptron.csv')\n",
    "x = df.iloc[:,[1,2]].values\n",
    "y=df.iloc[:, 0].values\n",
    "y= np.where(y=='spam',-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RKMJB7gJ7rpj",
    "outputId": "3d701a48-151c-40d3-82cd-d8e216c40808"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_regression = LinearRegression()\n",
    "linear_regression.fit(x,y)\n",
    "print(linear_regression.score(x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Linear Regression for spam detection.ipynb",
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
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