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This project involves visualizing the distribution of car sales data, focusing on various aspects such as car prices, annual incomes of customers, genders, car models, transmission types, and other relevant variables. The visualization is done using histograms and bar charts to gain insights into the dataset.

The dataset used for this project is the "car_sales_data.csv" file, containing information about individual car sales transactions. The dataset includes the following columns:

• Car_id: Unique identifier for each car sale transaction.
• Date: Date of the transaction.
• Customer Name: Name of the customer.
• Gender: Gender of the customer.
• Annual Income: Annual income of the customer.
• Dealer Name: Name of the dealer where the transaction occurred.
• Company: Car company or manufacturer.
• Model: Car model.
• Engine: Engine type.
• Transmission: Type of transmission (manual/auto).
• Color: Car color.
• Price ($): Price of the car.
• Dealer_No: Dealer number.
• Body Style: Style of the car body.
• Phone: Phone number of the dealer.
• Dealer_Region: Region where the dealer is located.

The following visualizations are included in this project:

1. Distribution of car prices: Histogram showing the distribution of car prices.
2. Distribution of annual incomes: Histogram showing the distribution of annual incomes of customers.
3. Distribution of genders: Bar chart showing the distribution of genders among customers.
4. Top 10 car models: Bar chart showing the top 10 car models based on frequency.
5. Distribution of transmission types: Bar chart showing the distribution of manual and automatic transmission types.
6. Top 3 car colors: Bar chart showing the top 10 car colors based on frequency.
7. Distribution of body styles: Bar chart showing the distribution of different body styles of cars.
8. Distribution of dealer regions: Bar chart showing the distribution of dealer regions.

To reproduce the visualizations:

1. Clone this GitHub repository to your local machine.
2. Ensure you have Python and necessary libraries installed (e.g., pandas, matplotlib).
3. Run the Jupyter Notebook file "car_sales.ipynb" to generate the visualizations.

• Smit Patel