This repository contains code and analysis for predicting Diwali sale trends using data analysis techniques. Diwali is one of the most significant festivals in India, marked by increased shopping and sales across various industries. This project aims to analyze historical Diwali sales data and build a predictive model to estimate the sales for upcoming Diwali festivals.

• Introduction
• Dataset
• Installation
• Usage
• Data Analysis
• Predictive Modeling
• Results
• Contributing
• License

Diwali is a festival of lights and celebrations in India, and it often sees a surge in consumer spending. This project focuses on analyzing historical Diwali sales data to identify trends, patterns, and factors that contribute to increased sales during this period. Additionally, a predictive model is built to estimate sales for future Diwali festivals, assisting businesses in making informed decisions.

The dataset used for this analysis is available in the data directory. It includes historical sales data, containing information such as date, product category, sales figures, and external factors like advertising expenses and discounts.

To run the code and analysis locally, you'll need the following dependencies:

• Python (>=3.6)
• Jupyter Notebook
• pandas
• matplotlib
• scikit-learn
• seaborn

Clone this repository to your local machine using:

git clone https://github.com/your-username/diwali-sale-prediction.git

Install the required dependencies using:

pip install -r requirements.txt

1. Navigate to the project directory.
2. Open the Jupyter Notebook Diwali_Sale_Prediction.ipynb to access the code and analysis.

The Diwali_Sale_Prediction.ipynb notebook contains detailed data analysis, including:

• Exploratory Data Analysis (EDA)
• Visualization of sales trends
• Correlation analysis between sales and external factors

The notebook also includes:

• Data preprocessing and feature engineering
• Building and training a predictive model using machine learning algorithms
• Model evaluation and validation

The project aims to achieve accurate predictions of Diwali sales figures based on historical data and external factors. The results and insights gained from the analysis can provide valuable information for businesses to optimize their strategies during the Diwali season.

Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to create an issue or submit a pull request.

This project is licensed under the MIT License.

Feel free to customize this README according to your project's specifics. Don't forget to include proper attribution for images or resources used. Good luck with your Diwali sale prediction data analysis! 🪔🛍️