Built a Machine Learning Model for the Customer Segmentation using PyCaret. PyCaret, a powerful Python library for automated machine learning. With PyCaret, I streamlined and accelerated the machine learning process, saving time and effort.
Dataset Link: https://www.kaggle.com/datasets/kaushiksuresh147/customer-segmentation?resource=download
I began by performing exploratory data analysis (EDA) on the dataset, gaining valuable insights into its structure and characteristics. These insights played a crucial role in providing the necessary parameters for the PyCaret setup function.
After completing the EDA, I leveraged PyCaret's comprehensive functionalities. The library allowed me to easily handle data preprocessing, including missing value imputation, feature scaling, and categorical encoding, ensuring the data was in the optimal format for analysis.
Next, PyCaret provided a wide range of machine learning algorithms to choose from. PyCaret's intuitive interface facilitated model comparison and hyperparameter optimization, ensuring I achieved the best possible performance.
The evaluation metrics obtained through PyCaret helped me assess the models accurately. I could analyze accuracy, precision, recall, and F1-score, gaining a comprehensive understanding of the models' performance.
I'm excited to present this successful implementation, showcasing the power of PyCaret in automating the machine learning pipeline while leveraging valuable insights gained through EDA.