SIMPLE LINEAR REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ce4c8e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b8893f98",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"Python/P14-Part2-Regression/Section 6 - Simple Linear Regression/Python/Salary_Data.csv\")\n",
    "dataset.head(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fa43e956",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d6c59de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ebd298df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e82c7545",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.33, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3a618f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression \n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3a09bcc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "799c6067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    "plt.scatter(x_train,y_train,color=\"red\")\n",
    "plt.plot(x_train,regressor.predict(x_train))\n",
    "plt.title(\"SALARY VS EXPERIENCE\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.xlabel(\"Experience\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "850a5b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_test,color=\"red\")\n",
    "plt.plot(x_test,y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76accab6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14bc8c2f",
   "metadata": {},
   "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",
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