I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

8.3. Regression diagnostics¶. Like R, Statsmodels exposes the residuals. That is, keeps an array containing the difference between the observed values Y and the values predicted by the linear model. A ...

Type python demo.py into terminal and you'll see the scattered data and best fit line.demo.py uses sklearn library for learning.. If you want learn how the linear regression works, you'd better read ...

In this video, we will implement linear regression in python from scratch. We will not use any build in models, but we will understand the code behind the linear regression in python. Your Lane to ...

Linear Trees combine the learning ability of Decision Tree with the predictive and explicative power of Linear Models. Like in tree-based algorithms, the data are split according to simple decision ...

5. Fitting Logistic Regression to the Training Set. Now we’ll build our classifier (Logistic). Import LogisticRegression from sklearn.linear_model; Make an instance classifier of the object ...

Time series regression usually involves independent variables other than a time-trend. However, the simple time-trend model is convenient for illustrating regression with autocorrelated errors, and ...

In this short course we will cover how to analyze simple and multiple linear regression models. You will learn concepts in linear regression such as: 1) How to use the F-test to determine if your ...