Day 39 of 75 DataSci.ipynb

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    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "full_health_data = pd.read_csv(\"data.csv\", header=0, sep=\",\")\n",
    "\n",
    "x = full_health_data[\"Duration\"]\n",
    "y = full_health_data [\"Calorie_Burnage\"]\n",
    "\n",
    "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
    "\n",
    "def myfunc(x):\n",
    " return slope * x + intercept\n",
    "\n",
    "mymodel = list(map(myfunc, x))\n",
    "\n",
    "print(mymodel)\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, mymodel)\n",
    "plt.ylim(ymin=0, ymax=2000)\n",
    "plt.xlim(xmin=0, xmax=200)\n",
    "plt.xlabel(\"Duration\")\n",
    "plt.ylabel (\"Calorie_Burnage\")\n",
    "\n",
    "plt.show()"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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