FBA_FML_AIforTrading Term1_Lesson 12 Quiz3.ipynb

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    "## Solution: Linear Regression\n",
    "\n",
    "Note that the LinearRegression().fit() expects 2D numpy arrays.  Since s1 and s2 are pandas series, we can use Series.values to get the values as a numpy array. Since these are 1D arrays, we can use numpy.reshape(-1,1) to make these 1000 row by 1 column 2 dimensional arrays.  \n",
    "\n",
    "The coefficients of the linear regression, $\\beta$ and $intercept$ for the regression line:  \n",
    "$y = \\beta \\times x + intercept$  \n",
    "Can be obtained after fitting to the data.  Use `LinearRegression.coef_[0][0]` for the slope and `LinearRegression.intercept_[0]` for the intercept."
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    "def regression_slope_and_intercept(xSeries, ySeries):\n",
    "    \"\"\"\n",
    "    xSeries: pandas series, x variable\n",
    "    ySeries: pandas series, y variable\n",
    "    \"\"\"\n",
    "    lr = LinearRegression()\n",
    "    #TODO: get the values from each series, reshape to be 2 dimensional\n",
    "    #set s1 to the x variable, s2 to the y variable\n",
    "    xVar = xSeries.values.reshape(-1,1)\n",
    "    yVar = ySeries.values.reshape(-1,1)\n",
    "    \n",
    "    #TODO: call LinearRegression.fit().  Pass in the x variable then y variable\n",
    "    lr.fit(xVar,yVar);\n",
    "    \n",
    "    #TODO: obtain the slope and intercept\n",
    "    slope = lr.coef_[0][0]\n",
    "    intercept = lr.intercept_[0]\n",
    "    \n",
    "    return (slope, intercept)\n",
    "\n",
    "quiz_tests.test_regression_slope_and_intercept(regression_slope_and_intercept);"
   ]
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    "slope, intercept = regression_slope_and_intercept(s1,s2);\n",
    "print(f\"slope {slope:.2f} and intercept {intercept:.2f}\")"
   ]
  }
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