LReg_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# provide data\n",
    "x = [[0, 1], [5, 1], [15, 2], [25, 5], [35, 11], [45, 15], [55, 34], [60, 35]]\n",
    "y = [4, 5, 20, 14, 32, 22, 38, 43]\n",
    "x, y = np.array(x), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a model and fit it\n",
    "model = LinearRegression().fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "coefficient of determination: 0.8615939258756775\n"
     ]
    }
   ],
   "source": [
    "#get the result\n",
    "r_sq = model.score(x, y)\n",
    "print('coefficient of determination:', r_sq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept: 5.52257927519819\n",
      "slope: [0.44706965 0.25502548]\n"
     ]
    }
   ],
   "source": [
    "print('intercept:', model.intercept_)\n",
    "print('slope:', model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response:\n",
      "[ 5.77760476  8.012953   12.73867497 17.9744479  23.97529728 29.4660957\n",
      " 38.78227633 41.27265006]\n"
     ]
    }
   ],
   "source": [
    "#predict response\n",
    "y_pred = model.predict(x)\n",
    "print('predicted response:', y_pred, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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