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

simple linear regression python sklearn | 1.77 | 0.4 | 241 | 85 | 39 |

simple | 0.9 | 0.3 | 9212 | 84 | 6 |

linear | 1.18 | 0.2 | 3258 | 68 | 6 |

regression | 1.94 | 0.1 | 8676 | 50 | 10 |

python | 0.02 | 0.6 | 820 | 33 | 6 |

sklearn | 0.28 | 0.1 | 795 | 38 | 7 |

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

simple linear regression python sklearn | 1.42 | 0.1 | 3636 | 58 |

simple linear regression sklearn | 0.39 | 0.1 | 408 | 75 |

python multiple linear regression sklearn | 1.97 | 0.2 | 856 | 32 |

non-linear regression python sklearn | 1.26 | 0.4 | 4261 | 87 |

simple linear regression using sklearn | 0.92 | 0.8 | 3564 | 90 |

Linear regression uses a method known as ordinary least squares to find the best fitting regression equation. Conversely, logistic regression uses a method known as maximum likelihood estimation to find the best fitting regression equation. Difference #4: Output to Predict. Linear regression predicts a continuous value as the output. For example: