### Tutorial: Linear Regression – Sklearn regression model (#3/281120191333)

November 28, 2019

In the previous part, we tried to build a model by trying to explain the level of carbon monoxide pollution based on temperature and pressure. […]

### Tutorial: Linear Regression – Tensorflow, calculation of R Square (#4/281120191525)

November 28, 2019

We continue to learn how to build multiple linear regression models. This time we will build a model using the Tensorflow library. As before, the […]

### Tutorial: Linear Regression – preliminary data preparation (#1/271120191024)

November 27, 2019

Part 1. Preliminary data preparation   AirQualityUCI Source of data: https://archive.ics.uci.edu/ml/datasets/Air+Quality In [1]: import pandas as pd df = pd.read_csv(‘c:/TS/AirQualityUCI.csv’, sep=’;’) df.head(3) Out[1]:   Date […]

### Tutorial: Linear Regression – Time variables and shifts. Use of offset in variable correlation (#2/271120191334)

November 27, 2019

Part 2. Simple multifactorial linear regression In the previous part of this tutorial, we cleaned the data file from the measuring station. A new, completed […]

### Linear regression with TensorFlow

November 26, 2019

Part one: Numpy method In [1]: import pandas as pd import tensorflow as tf import itertools   Source of data: https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant   Combined Cycle Power […]

### Jak uzupełnić brakujące dane w dataframe Python?

November 15, 2019

Linear Regression model in Python Sklearn part 1 [Polish Version] Jak uzupełnić brakujące dane w dataframe Python? Baza danych: AirQualityUCI Source of data: https://archive.ics.uci.edu/ml/datasets/Air+Quality In […]

### Example of the use of shift for linear regression in Python. How to find optimal correlation shift?

November 14, 2019

What is this the correlation shift? In supervised deep machine learning we have two directions: classification and regression. Regression needs continuous values of data. Because […]

### Perfect Plots: Bubble Plot

November 7, 2019

In [1]: import pandas as pd import matplotlib.pyplot as plt import numpy as np   Autos Source of data: https://datahub.io/machine-learning/autos In [2]: df2= pd.read_csv(‘c:/1/autos.csv’) df2.head() […]

November 5, 2019

### Perfect Plots: Individuals Control Chart I-MR

November 5, 2019

Energy Source of data: https://github.com/pyviz/holoviews/blob/master/examples/assets/energy.csv In [1]: import pandas as pd import matplotlib.pyplot as plt import numpy as np In [2]: df=pd.read_csv(‘c:/2/Energy.csv’) df.head() Out[2]: Unnamed: 0 Date […]

November 5, 2019

November 5, 2019