Overview

This repository houses a competitive data science project focused on predicting future sales using machine learning techniques. The goal of this project is to leverage historical sales data to build a robust predictive model that can forecast future sales accurately. The project is implemented in Python and utilizes various machine learning libraries and tools.

Key Features

Data Exploration: In-depth exploration of the provided dataset to understand its characteristics and patterns.

Feature Engineering: Crafting meaningful features from the raw data to enhance the predictive power of the model.

Model Selection: Experimentation with different machine learning models to identify the most suitable one for predicting future sales.

Hyperparameter Tuning: Fine-tuning model parameters to optimize performance and enhance predictive accuracy.

Evaluation Metrics: Utilizing relevant evaluation metrics to assess the performance of the models and compare different approaches.

Dependencies

-pandas -numpy -scikit-learn -matplotlib -seaborn

Acknowledgments

This project is inspired by a competitive data science challenge, and the code structure and techniques used are a result of collaboration and learning from the broader data science community.

Feel free to contribute, raise issues, or provide feedback to enhance the robustness and effectiveness of the predictive model. Happy coding!