Keyword | CPC | PCC | Volume | Score | Length of keyword |
---|---|---|---|---|---|

linear regression python pandas sklearn | 0.71 | 0.9 | 960 | 63 | 39 |

linear | 1.22 | 0.2 | 6099 | 95 | 6 |

regression | 1.84 | 0.4 | 7135 | 29 | 10 |

python | 1.99 | 0.4 | 6554 | 30 | 6 |

pandas | 0.06 | 0.6 | 5523 | 89 | 6 |

sklearn | 1.92 | 0.4 | 6459 | 38 | 7 |

Keyword | CPC | PCC | Volume | Score |
---|---|---|---|---|

linear regression python pandas sklearn | 0.37 | 0.5 | 826 | 40 |

multi linear regression python panda sklearn | 1.89 | 0.6 | 7080 | 30 |

linear regression python pandas | 1.49 | 0.4 | 760 | 95 |

linear regression in python using pandas | 0.17 | 0.7 | 8979 | 12 |

linear regression python sklearn | 0.53 | 0.5 | 241 | 21 |

python sklearn linear regression predict | 0.96 | 0.5 | 1689 | 24 |

linear regression model python sklearn | 0.55 | 0.7 | 4449 | 72 |

linear regression in pandas | 1.92 | 1 | 75 | 34 |

linear regression using pandas | 1.44 | 0.5 | 6659 | 21 |

pandas scikit learn linear regression | 0.79 | 0.5 | 724 | 96 |

multi linear regression python sklearn | 0.49 | 0.2 | 6555 | 63 |

linear regression sklearn pandas | 0.73 | 0.7 | 5930 | 88 |

python multiple linear regression sklearn | 1.72 | 1 | 1141 | 89 |

linear regression sklearn python | 0.36 | 0.2 | 2168 | 27 |

Python | Linear Regression using sklearn. Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting.

The first thing you have to do is split your data into two arrays, X and y. Each element of X will be a date, and the corresponding element of y will be the associated kwh. Once you have that, you will want to use sklearn.linear_model.LinearRegression to do the regression. The documentation is here. As for every sklearn model, there are two steps.

b0 =intercept of the line. b1, b2, … are coefficients. Independent variables are the features feature1 , feature 2 and feature 3. Dependent variable is sales. The equation for this problem will be: x1, x2 and x3 are the feature variables. In this example, we use scikit-learn to perform linear regression.

Code Explanation: model = LinearRegression () creates a linear regression model and the for loop divides the dataset into three folds (by shuffling its indices). Inside the loop, we fit the data and then assess its performance by appending its score to a list (scikit-learn returns the R² score which is simply the coefficient of determination ).