Code Snippet Repository

Today, we walk thru a basic machine learning pipeline including a train/test split and make use of a random search to conduct a hyperparameter tuning. This architecture is the backbone of many statistical machine learning tasks and hence usefuls as to keep close-by as a code-snippet!

Code Snippet Repository

I wrote a little Code Snippet that returns the “California Housing” dataset provided by sklearn as a named pandas dataframe. Ready for you to use in your applications.

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import numpy as np
import pandas as pd
from sklearn.datasets import fetch_california_housing

dict_data = fetch_california_housing()

list_names = dict_data.feature_names
list_names.extend(dict_data.target_names)

data = np.concatenate([dict_data.data, dict_data.target.reshape(-1,1)], axis=1)
df = pd.DataFrame(data=data, columns=list_names)

I need some Data - Now!

Often times, we come up with an idea for a visualization or simply want to try out a new model we have heard off. In short: We need data.

However, this can sometimes become a tedious problem as a real world dataset might need additional cleaning, which can be too much effort for a short test or playing around. Here, a toy dataset can come in handy. There are tons of toy-data sets out there, many good ones presented by the sklearn.dataset module. In the example above I remodel the arrays provided by fetch_california into a pd.DataFrame.

For more information on the “California Housing” dataset visit sklearn.datasets.fetch_california.