I developed customer behavior analysis in python where I used pandas for data cleaning, pre-processing, transformation, EDA and for in-depth analysis while plotly for data visualization. I used jupyter notebook for this project. Check it here: ujjwal01.com/index.php/custo… 𝗤𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝗰𝗿𝗶𝗽𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • Data Accuracy: Improves data accuracy by 99% through consistent imputation and mapping. • Efficiency: Reduces data preprocessing time by 30% with automated cleaning and transformations. • Consistency: Increases data consistency by 95% via uniform formatting (e.g., casing). • Outlier Detection: Identifies 100% of outliers using automated box plots. • Insight Generation: Speeds up insight generation by 50% with automated EDA and visualizations along with in-depth analyses. 𝗦𝗼𝗺𝗲 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗘𝗗𝗔 𝗮𝗻𝗱 𝗶𝗻-𝗱𝗲𝗽𝘁𝗵 𝗮𝗻𝗮𝗹𝘆𝘀𝗲𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • Average Spend by male is 986.93 and for female is 703.83 • 33.14% customers are unsatisfied. • Average Spend of gold members is 1311.14 USD and for bronze members, it is 473.39 • The average spend is dependent according to rating. • San Francisco has maximum average rating 4.81 and maximum items sold 1,160 while Houston has minimum average rating 3.19 and items sold 439. • Gold members have best average rating 4.68 and spend per item 74.5 USD while both are least for bronze members, average rating 3.32, spend per item 55.69 USD. • San Francisco has best spend by unit rating – 303.49 USD while it is least for Houston – 140.09 USD. • San Francisco has maximum young customers – 57 while Los Angeles and Houston have maximum middle customers. • Middle aged female customers purchase maximum products that is 124 while young male are at second position with 91 and it is least for senior females. • Satisfied Young customers are spending the most with average spend of 1376.9 USD, while it is least for Unsatisfied senior customers with average spend of 507.95 USD. #Python #Pandas #dataanalysisscript #dataanalysisautomation #analysisautomation #customeranalysis #dataanalysis #customerbehavioranalysis #datamanipulation #datacleaning #datapreprocessing #datatransformation #EDA #actionableinsight #dataanalyst #businessanalyst #dataanalytics #businessanalytics #datavisualization #BI #businessanalysis #dataanalysis #US #UK #USStartups #UKStartups