semvec/pypi-packages huggingface dataset:
mycustomersegmentation 0.1.0
A python package for customer segmentation
License: MIT license
Requires Python: >=3.6
Keywords: mycustomersegmentation

# Customer Segmentation Package

This package provides functions for customer data analysis and segmentation.

## Description and Features

The Customer Segmentation Package is designed to help analyze and segment customer data. It includes the following features:

- Loading customer data from a CSV file
- Finding missing values in the data
- Performing exploratory data analysis
- Plotting histograms and pair plots
- Converting string columns to datetime objects
- Performing K-means clustering on customer data
- Performing customer segmentation using K-means clustering
- Plotting scatter plots for each cluster based on two columns of data

## Installation

You can install the Customer Segmentation Package using pip:

```bash
pip install mycustomersegmentation
```

## Usage Examples

Here are some examples of how you can use the Customer Segmentation Package for customer segmentation:

```python
import pandas as pd
from mycustomersegmentation import *

# Load customer data
data = load_data("customer_data.csv")

# Find missing values
missing_values = find_missing_values(data)
print(missing_values)

# Perform exploratory data analysis
perform_eda(data)

# Plot histograms
columns = ["age", "income"]
plot_histograms(data, columns)

# Generate pairplot
plot_pairplot(data)

# Convert string column to datetime
data = convert_to_datetime(data, "date")

# Perform customer segmentation
num_clusters = 4
segmentation = customer_segmentation(data, num_clusters)
print(segmentation)

# Plot scatter plots for each cluster
column1 = "age"
column2 = "income"
plot_cluster_scatter(data, column1, column2, num_clusters)
```
## How it Can Be Used in Customer Segmentation

Customer segmentation is a common technique used in marketing and business analytics to divide a customer base into groups based on similar characteristics. This package provides a set of functions that can be used to analyze customer data, identify patterns, and perform clustering algorithms to segment customers into distinct groups. By understanding the different customer segments, businesses can tailor their marketing strategies, product offerings, and customer experiences to better meet the needs and preferences of each segment.

## License
This package is released under the MIT License. See [MIT](https://opensource.org/license/mit/).

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
Uncertainty-Sampler 1.0.0b0
Sampler based on where the model is least certain.
License: MIT license
Requires Python: <4,>=3.8

# AutoRA Uncertainty Sampler

The uncertainty sampler identifies experimental conditions $\vec{x}' \in X'$ with respect model uncertainty. Within the uncertainty sampler, there are three methods to determine uncertainty:

## Least Confident
$$
x^* = \operatorname{argmax} \left( 1-P(\hat{y}|x) \right),
$$

where $\hat{y} = \operatorname{argmax} P(y_i|x)$

## Margin

$$
x^* = \operatorname{argmax} \left( P(\hat{y}_1|x) - P(\hat{y}_2|x) \right),
$$

where $\hat{y}_1$ and $\hat{y}_2$ are the first and second most probable class labels under the model, respectively.

## Entropy
$$ 
x^* = \operatorname{argmax} \left( - \sum P(y_i|x)\operatorname{log} P(y_i|x) \right)
$$

# Example Code

```
from autora.experimentalist.sampler.uncertainty_sampler import uncertainty_sampler
from sklearn.linear_model import LogisticRegression
import numpy as np

#Meta-Setup
X = np.linspace(start=-3, stop=6, num=10).reshape(-1, 1)
y = (X**2).reshape(-1)
n = 5

#Theorists
lr_theorist = LogisticRegression()
lr_theorist.fit(X,y)

#Sampler
X_new = uncertainty_sampler(X, lr_theorist, n, measure ="least_confident")
```