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https://github.com/aisurjyasamantaray/sales-perfomance-analysis-dashboard

A comprehensive sales performance analysis dashboard built using Python, and visualization tools. This project includes data cleaning, descriptive statistics, correlation analysis, and insights into sales trends, profitability, and the impact of discounts. Key features include interactive visualizations using Seaborn, and Matplot
https://github.com/aisurjyasamantaray/sales-perfomance-analysis-dashboard

analytics annova data data-analysis data-visualization-project dataproject eda hypothesis-testing pandas-dataframe python sales-performance-analysis statistics

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A comprehensive sales performance analysis dashboard built using Python, and visualization tools. This project includes data cleaning, descriptive statistics, correlation analysis, and insights into sales trends, profitability, and the impact of discounts. Key features include interactive visualizations using Seaborn, and Matplot

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README 

        
This project analyzes sales data to extract meaningful insights regarding profitability, customer behavior, and the impact of discounts across different regions.



Data Cleaning and Preprocessing



Handling Missing Values: Identified and addressed missing data.



Outlier Detection and Removal: Interquartile Range (IQR) was used to detect and remove outliers.



Normalization and Standardization: Ensured data was appropriately scaled for analysis.



Descriptive Statistics



Summary Statistics: Calculated mean, median, standard deviation, and kurtosis for numerical variables.



Data Distribution Analysis: Visualized distributions with histograms, boxplots, and density plots. Checked for skewness and kurtosis.



Correlation Analysis

Pearson Correlation: Initially considered for numerical variables but found unsuitable due to non-normal distribution.

Spearman Correlation: Used to assess monotonic relationships. Key Insight: Found a strong negative correlation between discount levels and profit margins.



Analysis of Variance (ANOVA)

One-Way ANOVA: Analyzed the difference in profit across regions and random regional groups.



Conducted normality checks (QQ plots, Shapiro-Wilk test) and homogeneity of variance (Levene's test).



Result: Determined whether there were statistically significant differences in mean profits among different regions.



Kruskal-Wallis Test



Non-parametric Test: Used since data did not meet ANOVA assumptions.



Result: Analyzed differences in profit medians across regions.



Profitability and Regional Analysis



Top and Bottom Cities by Profit: Identified cities with the highest and lowest profit margins.

Regional Profitability: Compared total profits across different regions. Analyzed monthly profit trends to understand seasonality.



Impact of Discounts on Profit

Discount and Profit Relationship: Analyzed the impact of discount levels on profit margins.

Key Insight: Found a significant negative correlation between higher discounts and lower profits.



Shipping Cost Analysis

Cities with Highest and Lowest Shipping Costs: Identified cities with varying shipping costs.

Average Shipping Cost: Compared shipping costs across cities.



Customer Analysis

Number of Customers by City: Analyzed customer distribution across cities.

Customer Segmentation: Grouped customers based on regions and categories.



Product Analysis

Top-Selling Products: Identified products with the highest sales volumes.

High vs. Low Margin Products: Differentiated between products with high and low profit margins.