This data analysis project demonstrates my proficiency in extracting valuable insights from a comprehensive car sales dataset sourced from Kaggle. Similar to my prior work on US traffic accidents, where the dataset spanned from February 2016 to March 2023, the current analysis dives into the dynamic world of car sales. Through a combination of critical thinking, meticulous data cleaning, thorough analysis, and effective visualization, this project aims to uncover patterns and trends within the provided dataset.

In the realm of car sales, understanding the underlying factors that influence customer choices, dealer success, and market trends is paramount. The goal of this analysis is to extract actionable insights from the provided dataset, shedding light on critical aspects such as popular car models, customer demographics, and regional variations in sales. By addressing these key questions, we aim to empower stakeholders in the automotive industry to optimize marketing strategies, tailor inventory management, and enhance overall business performance.

The primary objective of this data analysis project is to provide a comprehensive understanding of the car sales dataset and offer actionable insights for strategic decision-making in the competitive landscape of the automotive industry. This project showcases the application of critical data analysis skills and tools, including SQL for initial cleaning and Tableau for detailed visual analysis.

Popular Car Models: Which car models are the most popular in terms of sales volume, and what are their key characteristics?

Demographic Analysis: How does the distribution of annual income and gender vary among the customers purchasing cars? Are there any notable trends or preferences?

Temporal Trends: What are the monthly and yearly trends in car sales? Are there any seasonal patterns that can be identified?

Dealer Performance: Which dealerships exhibit the highest sales volume, and how does their performance vary across different regions?

Regional Disparities: Are there significant variations in car sales across different regions? What factors may contribute to these regional disparities?

Feel free to explore the project, review the analysis process, and contribute to further enhancements. Your insights and feedback are highly appreciated!

The dataset utilized in this analysis project was sourced from Kaggle, a popular platform for datasets and data science competitions. Specifically, the dataset covers a comprehensive collection of car sales transactions and associated details. The original dataset, contributed by Kaggle user Vasu Avasthi, was obtained from Kaggle's dataset repository.

I will use the select statement to examine a sample of rows from each table. This will allow me to understand the data types, ranges, and overall structure of my data table.

From looking at this I can see that I have 16 unique columns that contain various unique values. I can see that I have the Date of purchase for each sale the region of the sale and the various information about the cars that were purchased.

Now I will Identify columns with missing values. I can do this by using 'COUNT' and 'IS NULL' Conditions to assess missing values.

I did this for every column and there were no missing values

I will check for duplicatees to do this I need to identify duplicate rows in the dataset. Since column Car_id is a unique identifier for each row and should not have duplicate values I can use it to check for the presence of duplicate rows.

As you can see there are no duplicated rows

Despite the cleanliness of the dataset, it's crucial to recognize that real-world data often presents challenges. In instances where duplicate, missing, or null data might be encountered, this assessment phase would typically be the stage to strategize and implement solutions. Common actions include:

Duplicate Data: Identification and removal or consolidation based on business rules.

Missing or Null Data: Decision on imputation methods, deletion, or acknowledgment in subsequent analysis.

Fortunately, the current dataset did not require such interventions, allowing for a streamlined transition to the subsequent phases of data exploration and analysis.

The absence of data quality issues during the assessment stage is a positive indicator, facilitating a more efficient and focused analysis process.

I will write a query that will count the number of Unique Companies

There are 30 Different companies within the data table

Colors: I will now write a query that shows how many different distinct colors there are in the dataset

There are only 3 unique color options for cars within the data table

Models and their Counts: I will write a query that will show me the unique models and how many of the sales are along with them

This allowed me to see that there are 154 different models and how many sales each of the models have

Summary Statistics for 'Price' I will get summary statistics for price by finding out the average price, the minimum prize and the max price

Summary Statistics for 'Annual Income' I will also get the average min and max for the annual income column

This query uses the YEAR(Date) and Month(DATE) to extract the year and month from the 'Date' Column

This shows me that 2023 had more sales than 2022

**Grouped Analysis By Gender - Average Price and Sales Count

Welcome to the Car Sales Analysis Dashboard! This interactive dashboard has been meticulously crafted to provide compelling insights into our car sales data. From high-level Key Performance Indicators (KPIs) to detailed trends, it offers a comprehensive view of our sales landscape.

Features KPIs YTD Total Car Sales: An overview of the year-to-date total car sales.

1.) YTD Total Sales by Body Type: Visualize how our sales vary across different body types.

2.) YTD Total Sales by Color: Explore the distribution of sales based on color preferences.

3.) Total Sales by Region: Understand the geographical impact on our sales.

4.) Company-Wide Sales Trend: Track the overall sales trend across different time periods.

Filters The dashboard includes interactive filters, empowering you to tailor the view according to your specific interests:

• Transmission: Filter by transmission type.

• Body Style: Explore sales based on different body styles.

• Engine: Dive into the data based on engine specifications.

• Gender: Analyze sales trends based on customer gender.

• Date: Customize the view by selecting a specific date range.

1.) YTD Total Car Sales Growth

I observed a significant positive trend in our total car sales, showing an impressive growth of 23.59%. The number of cars sold also experienced a notable increase, currently standing at 24.57%. Additionally, I noticed a marginal decrease of approximately 0.79% in the average sale price compared to the previous year.

2.) Body Type Preferences

The data unequivocally highlights the dominance of SUVs in our sales landscape. Notably, the top three performing body types are SUV, Sedan, and Hatchback, showcasing their popularity among our customers.

3.) Color Impact on Sales

The data underscores a clear color preference, with pale white emerging as the top performer among the three selections in the dataset. Notably, black lags behind white by approximately 49 million in sales, while red trails black by about 54 million.

4.) Regional Sales Dynamics

The data reveals a notable disparity in sales performance, with Austin, Janesville, and Scottsdale emerging as standout performers compared to other regions.

5.) Company-Wide Sales Trend Analysis:

The data illuminates a compelling trend: our strongest sales numbers consistently appear during Q3 and Q4. Additionally, it indicates a heightened likelihood of car purchases towards the middle and end of the year.

1.) YTD Total Car Sales Growth

Simultaneously, while the average sale price has experienced a marginal decrease of approximately 0.79%, this could present an opportunity to attract a wider customer base. Consider implementing targeted promotions, bundled deals, or exclusive discounts to drive sales and maintain our competitive edge in the market. By strategically aligning our marketing efforts with the positive sales trend and addressing pricing dynamics, we can further enhance our market position and drive sustained growth in the coming quarters.

2.) Body Type Preferences

To capitalize on this trend, consider focusing marketing efforts and inventory management towards these top-performing body types. Additionally, exploring opportunities to enhance the appeal of passenger and hard top options could further diversify our product offerings and potentially drive increased sales in these segments.

3.) Color Impact on Sales

To capitalize on the popularity of pale white and address the sales gap, consider strategic marketing initiatives to elevate the appeal of black and red. This could involve targeted promotions, exclusive offers, or visual merchandising enhancements to stimulate increased sales in these color categories.

4.) Regional Sales Dynamics

For regions below the top three, which exhibit similar sales numbers, there's an opportunity to analyze and implement successful tactics from the leading regions. Consider conducting a detailed analysis of the successful regions to identify key factors contributing to their strong performance and replicate these strategies in regions with similar sales numbers to drive overall growth."

5.) Company-Wide Sales Trend Analysis:

To capitalize on this seasonal trend, consider aligning marketing campaigns, promotions, and inventory planning to strategically target the heightened purchasing behavior observed in Q3 and Q4. This insight offers a valuable opportunity to optimize resource allocation and drive increased sales during these key periods."