# 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
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"description": "# Customer Segmentation Package\n\nThis package provides functions for customer data analysis and segmentation.\n\n## Description and Features\n\nThe Customer Segmentation Package is designed to help analyze and segment customer data. It includes the following features:\n\n- Loading customer data from a CSV file\n- Finding missing values in the data\n- Performing exploratory data analysis\n- Plotting histograms and pair plots\n- Converting string columns to datetime objects\n- Performing K-means clustering on customer data\n- Performing customer segmentation using K-means clustering\n- Plotting scatter plots for each cluster based on two columns of data\n\n## Installation\n\nYou can install the Customer Segmentation Package using pip:\n\n```bash\npip install mycustomersegmentation\n```\n\n## Usage Examples\n\nHere are some examples of how you can use the Customer Segmentation Package for customer segmentation:\n\n```python\nimport pandas as pd\nfrom mycustomersegmentation import *\n\n# Load customer data\ndata = load_data(\"customer_data.csv\")\n\n# Find missing values\nmissing_values = find_missing_values(data)\nprint(missing_values)\n\n# Perform exploratory data analysis\nperform_eda(data)\n\n# Plot histograms\ncolumns = [\"age\", \"income\"]\nplot_histograms(data, columns)\n\n# Generate pairplot\nplot_pairplot(data)\n\n# Convert string column to datetime\ndata = convert_to_datetime(data, \"date\")\n\n# Perform customer segmentation\nnum_clusters = 4\nsegmentation = customer_segmentation(data, num_clusters)\nprint(segmentation)\n\n# Plot scatter plots for each cluster\ncolumn1 = \"age\"\ncolumn2 = \"income\"\nplot_cluster_scatter(data, column1, column2, num_clusters)\n```\n## How it Can Be Used in Customer Segmentation\n\nCustomer 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.\n\n## License\nThis package is released under the MIT License. See [MIT](https://opensource.org/license/mit/).\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n",
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