——Exploring the “Ontario Climate Change Data Portal”: A Case Study in Data Exploration Prompts searchable table Examples Download csv file from internet ChatGPT prompt I want you to act as a Python developer tasked with downloading a CSV file from the internet using the provided URL: https://lamps.math.yorku.ca/OntarioClimate/data/content/grids/Historical/Monthly_2m_temperature_8964_ERA5_January1981toMarch2021.csv. To accomplish this, write a Python script that ..
Category : Numpy
This example pytho program define the popular activation functions and display the functions. Code: import numpy as np import matplotlib.pyplot as plt #——————————- # define the activation functions def relu(x): v=x.copy() v[x=0]=x[x>=0] v[x=alpha*x] ..
Activation function is a very important component of machine learning and deep learning. There are three types of Activation Functions: Binary Step Function; Linear Activation Function and Non-Linear Activation Functions. Available activation functions in Keras include relu, sigmoid, softmax, softplus, softsign, tanh, selu, elu and exponential. In addition,user can also create custom activation function.Below is an ..
Sample code for reducing overfitting problems in deep learning.Answer the following questions: import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # create range of monthly dates download_dates = pd.date_range(start=’2019-01-01′, end=’2020-01-01′, freq=’MS’) # URL from Chrome DevTools Console base_url = (“https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&” “stationID=51442&Year={}&Month={}&Day=7&timeframe=1&submit=Download+Data”) # add format option to year ..
Example code for a regression model with multiple layers. In addition to the input of the first layer, it keeps adding new inputs to the later layers. Prepare data import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # create range of monthly dates download_dates = pd.date_range(start=’2019-01-01′, end=’2020-01-01′, ..
Example code to transform continuous numerical variables into discrete variables with different methods. It cab also answer the following questions. Prepare data and load functions Code import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from feature_engine.discretisation import EqualFrequencyDiscretiser from feature_engine.discretisation import EqualWidthDiscretiser from feature_engine.discretisation import ArbitraryDiscretiser from ..
Example code for creating and adding new features to a data frame using the feature-engine. It also answer following questions: Math features Code import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from feature_engine.creation import MathFeatures from feature_engine.creation import RelativeFeatures from feature_engine.creation import CyclicalFeatures # create range of ..
Example code for handling outlier with 3 methods of feature-engine. Winsorizer Caps maximum and/or minimum values of a variable at automatically determined values.[ref:https://feature-engine.readthedocs.io/en/latest/user_guide/outliers/Winsorizer.html] Code import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from feature_engine.outliers import Winsorizer # Load dataset def load_titanic(): data = pd.read_csv(‘https://www.openml.org/data/get_csv/16826755/phpMYEkMl’) data = data.replace(‘?’, ..
The sample code shows you how to encode categorical data and answer the following questions: One hot encoder Replaces the categorical variable by a group of binary variables which take value 0 or 1, to indicate if a certain category is present in an observation. Example code import numpy as np import pandas as pd ..
Example python code for handling missing data (ref:Python feature engineering cookbook ). Also answer the following questions: import pandas as pd from sklearn.model_selection import train_test_split from sklearn.impute import SimpleImputer from feature_engine.missing_data_imputers import MeanMedianImputer from feature_engine.imputation import ArbitraryNumberImputer from feature_engine.imputation import EndTailImputer from feature_engine.imputation import CategoricalImputer from feature_engine.imputation import RandomSampleImputer from feature_engine.imputation import AddMissingIndicator from feature_engine.imputation ..