02_b_Linear_Regression_sklearn.ipynb

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    "# Linear Regression excercise with sklearn\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#Reading data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = pd.read_csv('headbrain.csv')\n",
    "x = data['Head Size(cm^3)'].values\n",
    "y = data['Brain Weight(grams)'].values\n",
    "n = len(x)\n",
    "\n",
    "# cannot use 1D array => reshape\n",
    "x=x.reshape((n,1))\n",
    "\n",
    "# model create\n",
    "reg = LinearRegression()\n",
    "\n",
    "#fit training data\n",
    "reg = reg.fit(x,y)\n",
    "y_pred = reg.predict(x)\n",
    "\n",
    "# calc rms error and r2\n",
    "rmse = np.sqrt(mean_squared_error(y,y_pred))\n",
    "r2=reg.score(x,y)\n",
    "\n",
    "print(rmse)\n",
    "print(r2)"
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