Zabezpieczone: Tensorflow – LINEAR REGRESSION (base example)
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Tensorflow vs. Sklearn – Comparison of linear classifiers [B2/1]
[B2/1] How to survive on the Titanic? Long time I was thinking, that this sample is a something as any railway statistic or other theoretical […]
Zabezpieczone: TENSORFLOW LINE CLASSIFICATOR (base example)
Brak zajawki, ponieważ wpis jest zabezpieczony hasłem.
Tensorflow – Calculation of R square for linear regression
Parking Birmingham occupancy Source of data: https://archive.ics.uci.edu/ml/datasets/Parking+Birmingham In [35]: import pandas as pd df = pd.read_csv(’c:/TF/ParkingBirmingham.csv’) df.head(3) Out[35]: SystemCodeNumber Capacity Occupancy LastUpdated 0 BHMBCCMKT01 577 61 […]
Improving the linear regression model using synthetic variables Part. 1
EN031220191208 Parking Birmingham occupancy analysis Source of data: https://archive.ics.uci.edu/ml/datasets/Parking+Birmingham In [1]: import pandas as pd df = pd.read_csv(’c:/TF/ParkingBirmingham.csv’) df.head(3) Out[1]: SystemCodeNumber Capacity Occupancy LastUpdated […]
Polynomial regression model
Polynomial regression model Polynomial regression model is technically a special case of multiple linear regression. This is definitions and explanation found in Wikipedia: https://en.wikipedia.org/wiki/Polynomial_regression. Just […]
Tutorial: Linear Regression – Sklearn regression model (#3/281120191333)
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)
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)
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)
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
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?
Jak uzupełnić brakujące dane w dataframe Python? Baza danych: AirQualityUCI Source of data: https://archive.ics.uci.edu/ml/datasets/Air+Quality In [47]: import pandas as pd df = pd.read_csv(’c:/TS/AirQualityUCI.csv’, sep=’;’) df.head(3) […]
Example of the use of shift for linear regression in Python. How to find optimal correlation shift?
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
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() Out[2]: […]
Perfect Plots: Subplots
In [1]: import pandas as pd import matplotlib.pyplot as plt import numpy as np Economics In [2]: df=pd.read_csv(’c:/1/economics.txt’) df.head() Out[2]: date pce pop psavert […]