The Predict Future Sales aims to forecast sales for a retail store using historical sales data from a Kaggle competition. The goal is to build predictive models that can accurately estimate future sales based on various features, including product details, store information, and historical sales trends.
The dataset includes several CSV files, such as:
- sales_train.csv: Contains daily historical sales data, including product IDs, store IDs, sales amounts, and dates.
- items.csv: Contains information about the items, including their IDs and categories.
- stores.csv: Provides details about the stores involved in the sales.
- test.csv: The test set for making predictions, which includes product IDs and store IDs but lacks sales figures.
Key features of the project include:
- Data Preprocessing: Cleaning and transforming the data for analysis, including handling missing values and converting date formats.
- Exploratory Data Analysis (EDA): Visualizing data to identify patterns and relationships that can inform the modeling process.
- Feature Engineering: Creating new features that can improve the model's predictive power, such as aggregate sales metrics and time-based features.
- Modeling: Implementing various machine learning algorithms (e.g., linear regression, decision trees, random forests) to forecast sales.
- Evaluation: Assessing model performance using metrics like RMSE (Root Mean Squared Error) and comparing different models.
To get started with the project, follow these steps:
- Clone the repository:
git clone https://github.com/yourusername/PredictFutureSales.git