This Python project delves into the analysis of Diwali sales data, aiming to extract insights beneficial for augmenting customer experience and driving sales. Employing diverse data analysis techniques, it uncovers patterns, trends, and crucial metrics associated with Diwali sales.
Diwali, a celebrated festival, serves as a pivotal period for businesses. Analyzing sales data during this juncture offers invaluable insights. This project harnesses Python and data analysis libraries to:
- Gain insights into customer behavior during Diwali.
- Identify popular products and categories.
- Analyze sales trends and patterns.
- Offer actionable insights for enhancing customer experience and boosting sales.
- Data cleaning and preprocessing.
- Exploratory data analysis (EDA) for pattern recognition.
- Visualization of sales trends using matplotlib and seaborn.
- Statistical analysis for identifying significant factors.
- Recommendations for improving customer experience and sales.
# Clone the repository:
git clone https://github.com/iamtanmay07/diwali-sales-analysis.git
# Navigate to the project directory:
cd diwali-sales-analysis
# Install the required dependencies:
pip install -r requirements.txtThe main analysis is conducted in Jupyter notebooks within the notebooks directory. Each notebook concentrates on specific facets of Diwali sales data analysis, encompassing data cleaning, exploratory data analysis, visualization, and recommendations.
The main analysis is conducted in Jupyter notebooks within the notebooks directory. Each notebook delves into specific aspects of Diwali sales data analysis, encompassing data cleaning, exploratory data analysis, visualization, and recommendations.
The project yields actionable insights and recommendations beneficial for businesses aiming to augment customer experience and enhance sales during the Diwali festival.