time_series_analysis

Domain: Sales

time series analysis is an important skill for a data analyst. especially if you work in industries where data changes over time (e.g., finance, economics, operations, sales, or marketing).

dataset overview

this dataset features 10,000 records of used car sales from 2015 to 2024, capturing various aspects of the sales process. it includes details such as car make, model, distributor, location, and pricing. The dataset is designed to aid in analyzing trends in used car sales, including price fluctuations, sales patterns, and agent performance. Each record is enriched with attributes like mileage, engine power, and sale status, providing a comprehensive view of the used car market over the decade. this dataset is ideal for data analysis projects, offering insights into the automotive sales industry. feel free to explore the data and uncover patterns that could inform decision-making in car sales and marketing.

for detailed description please check my GitHub, Kaggle & Blog

potential time series variables in dataset

Purchased Date
Sold Date
Manufactured Year
Price-$
Sold Price-$
Margin-%
Sales Commission-$
Feedback (quantified)
Month/Year

types of time series analysis performed on the dataset

trend analysis

• identify whether car sales are increasing or decreasing over the years.
  
• examine pricing trends to understand how used car market values are changing.
  

seasonality analysis

• look for recurring patterns (e.g., higher sales during specific months or quarters).
  
  • advanced analysis like comparing monthly trends across years
    
• analyze seasonal demand for certain car types (SUVs in winter, convertibles in summer).
  
  • advanced analysis by price and year
    

forecasting

• forecast future sales, pricing, or margin trends using historical data.
  
  • forecast sales using ARIMA
    
• predict peak sales periods or distributor inventory needs.
  
  • advanced approaches like prophet:use prophet for seasonality-aware forecasting
    

correlation and causation

• examine relationships between variables over time (e.g., Does higher sales commission correlate with more sold cars?).
  
  • advanced analysis regression
    

customer behavior analysis

• study feedback trends over time to evaluate improvements in customer satisfaction.
  
  • advanced analysis like compare before and after initiatives
    
  • sentiment analysis for text feedback
    
• examine whether sales ratings correlate with higher margins or quicker sales.
  

Thank you!