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diwali_analysis_python

Diwali Sales Analysis on Jupyter Notebook - Cleaning and Exploratory Analysis:

In this Jupyter Notebook project focusing on Diwali sales, I initiated the analysis with meticulous data cleaning to ensure a robust foundation for insights. The step-by-step process included:

1)Identifying and Handling Blank Columns:

Began by identifying and flagging columns with missing or blank values. Removed irrelevant or empty columns to streamline the dataset.

2)Null Values Removal:

Conducted thorough null value analysis to identify and understand data gaps. Implemented strategies to handle null values, ensuring data completeness. Gender-Based Spending Analysis:

Investigated the distribution of customers based on gender. Utilized graphical representations to showcase the gender-wise spending patterns during Diwali.

3)Top Spenders Analysis:

Identified and analyzed the highest spenders in the dataset. Visualized spending patterns to understand customer behavior and preferences.

4)Age Group Spending Patterns:

Categorized customers into age groups for targeted analysis. Explored which age groups exhibited the highest spending tendencies during the festive season. State-wise Order Analysis:

Conducted a comprehensive analysis of orders based on geographical locations (states). Visualized the distribution of orders per state to identify regions with the highest sales.

5)Category-wise Order Distribution:

Segmented orders by product categories to discern popular choices. Created graphical representations to highlight which product categories dominated Diwali sales.

Key Findings:

Revealed insights into customer demographics, aiding in personalized marketing strategies. Uncovered spending patterns across different age groups, guiding targeted promotions. Identified regions with high-order volumes, facilitating logistical and distribution planning. Prioritized product categories based on popularity, influencing inventory management. This Jupyter Notebook analysis provided a deep dive into the Diwali sales dataset, offering actionable insights for strategic decision-making, marketing, and resource allocation during the festive season.

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