kuttychurn 0.1.0

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churn-prediction

AI Project based on the E-Commerce churn Prediction

Project Organization

Churnprediction/ │ ├── README.md # Project overview and instructions. │ ├── data/ # Where data is stored. │ ├── raw/ # The original, immutable data dump. │ ├── interim/ # Intermediate data that has been transformed. │ └── processed/ # The final, canonical datasets for modeling. | ├── notebooks/ # Jupyter notebooks for exploration and presentation. | ├── 01_exploratory_data_analysis.ipynb | ├── 02_data_preparation_and_feature_engineering.ipynb | └── 03_modeling_and_evaluation.ipynb │ ├── reports/ # Generated analysis as HTML, PDF, LaTeX, etc. │ └── figures/ # Generated graphics and figures to be used in reporting. | ├── requirements.txt # The requirements file for reproducing the analysis environment. | ├── churn_predictor/ | ├── init.py | ├── data_preparation.py # Contains code for loading the dataset, handling missing values, and splitting the data. | ├── feature_engineering.py # Handles encoding of categorical variables and any necessary feature transformation. | ├── model_training.py # Contains the initialization and training of the machine learning model, Random Forest Classifier, along with model evaluation. | ├── predict.py # Utilized for making predictions on new data using the trained model. | └── visualization.py # Contains functions for visualizing model performance and feature importances. | └── tox.ini # tox file with settings for running tox; see tox.testrun.org

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To create a README.md file for this churn prediction project, I'll highlight the main sections that should be included in the readme file. Additionally, I'll list the necessary packages for the churn prediction project, based on the analysis and model building steps that we've performed.

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Project Overview

This project aims to predict customer churn for an E-Commerce platform. Using machine learning techniques, specifically Random Forest Classifier, we analyze customer behavior and other relevant features to identify patterns that indicate the likelihood of churn.

Setup and Installation

Ensure you have Python installed on your system. This project is built using Python 3.8 or higher.

1. Clone the repository to your local machine.
2. Navigate into the project directory.
3. Install the required packages using the requirements.txt file provided in the project directory.

pip install -r requirements.txt

The Python version used is 3.9.17. Based on the analysis and model building steps performed in the project, the requirements.txt file should include the following packages:

numpy
pandas
scikit-learn
matplotlib
openpyxl
xlrd

Data

The dataset, "E Commerce Dataset.xlsx", consists of various customer attributes and their interaction with the E-Commerce platform. Key features include CustomerID, Churn, Tenure, PreferredLoginDevice, CityTier, and many more.

KuttyChurn Package

KuttyChurn is a Python package developed for predicting customer churn in E-commerce datasets. It encompasses a comprehensive workflow from data preparation to model evaluation and prediction, allowing users to efficiently process data, train machine learning models, and visualize important features influencing customer retention.

Installation

Clone this repository to your local machine using:

git clone <repository-url>

Navigate to the project directory and install the package using pip:

cd KuttyChurn
pip install .

Components

• data_preparation.py: Script for loading and initially cleaning the raw E-commerce dataset.
• feature_engineering.py: Contains methods for feature extraction and preprocessing, ensuring data is suitable for model training.
• model_training.py: Facilitates the training of machine learning models using the preprocessed data, with a focus on Random Forest Classifier.
• predict.py: Provides functionality for making predictions on new data using the trained model.
• visualization.py: Offers several plotting functions to visualize the data distribution, model metrics, and feature importances.

Usage

While it's recommended to explore individual scripts for granular control over the process, the package is designed to be used as follows:

1. Prepare your dataset using the data_preparation module.
2. Employ the feature_engineering module to process your dataset further.
3. Train your model with the model_training module.
4. Use the predict module for making predictions.
5. Visualize your model's performance using the visualization module.

Running the Project

To run the project, execute the main.py script. This will perform data preparation, feature engineering, model training, and evaluation in sequence.

main.py file :

'''python

Pretend this is main.py

from KuttyChurn.data_preparation import load_data from KuttyChurn.data_preparation import impute_missing_values from KuttyChurn.feature_engineering import encode_categorical_variables from KuttyChurn.data_preparation import split_data from KuttyChurn.model_training import evaluate_model from KuttyChurn.model_training import train_model

Note: These function calls reference the structure described earlier,

but we will simulate these steps directly here due to environmental constraints.

Data Preparation

ecommerce_data_loaded = load_data('../data/raw/E Commerce Dataset.xlsx') ecommerce_data_imputed = impute_missing_values(ecommerce_data_loaded) ecommerce_data_encoded = encode_categorical_variables(ecommerce_data_imputed) X_train, X_test, y_train, y_test = split_data(ecommerce_data_encoded, 'Churn')

Feature Engineering

(In this case, encoding was done during data preparation)

Model Training

model = train_model(X_train, y_train) accuracy, report = evaluate_model(model, X_test, y_test)

Prediction

(Not applicable for this demo; typically, you'd use make_predictions with new data)

print(f"Accuracy Score: {accuracy}") print("Classification Report:") print(report) '''

python main.py

Contributing

Contributions to this project are welcome. Please ensure to follow best practices for code quality and consistency.

LICENSE

For the MIT license, the contents would be as follows:

MIT License

Copyright (c) 2024 Kishorekumar

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Contribution

Contributions are welcome! Please read the contribution guidelines for more information.

Authors

• Kishorekumar([e-mail])

Thank you for using Churn Predictor for your customer churn analysis needs!