Lab 22- Linear Regression - Demo I.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4bfd3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6e1304c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 2 * np.random.randn(100,1) + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4331c928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.72008176],\n",
       "       [5.15746179],\n",
       "       [1.20737324],\n",
       "       [2.04484293],\n",
       "       [0.72953343],\n",
       "       [0.44395607],\n",
       "       [2.59198972],\n",
       "       [5.82553833],\n",
       "       [3.35134684],\n",
       "       [0.74992481]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5628181f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 256 + 6 * x + np.random.randn(100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b11050ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[289.90958244],\n",
       "       [288.30846641],\n",
       "       [261.73270091],\n",
       "       [269.97637145],\n",
       "       [260.9251103 ],\n",
       "       [259.17686184],\n",
       "       [273.37356586],\n",
       "       [291.10846613],\n",
       "       [277.05034752],\n",
       "       [260.34011845]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a63b579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'b^')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f0d0824",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression().fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bfaa153f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[256.12455612] [[5.98137673]]\n"
     ]
    }
   ],
   "source": [
    "print(model.intercept_, model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a039015b",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_house = 5\n",
    "m = model.coef_\n",
    "c = model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bafaa3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[286.03143978]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m * new_house + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "501418bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[286.03143978]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb7c1021",
   "metadata": {},
   "outputs": [],
   "source": [
    "line = np.linspace(0, 10, 100).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2c63977",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8e3d62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28f76ba7970>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(line, predictions, c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ad9d864",
   "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",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}