Demographic Analysis and Bike Purchase Prediction
This project aims to analyze a dataset containing demographic information of individuals and predict whether they are likely to purchase a bike.The project involves exploring the dataset, creating a pivot table to gain insights into the relationships between different variables, and building a dashboard to visualize the data using bar charts, line charts, and slicers. The dashboard provides a user-friendly interface to explore and understand the patterns and trends within the dataset.
By analyzing the demographic data and predicting bike purchases, this project aims to provide valuable insights for marketing and business strategies in the bike industry.
The dataset used in this project consists of the following columns:
- ID: Unique identifier for each individual.
- Marital Status: The marital status of the individual.
- Gender: The gender of the individual.
- Income: The income of the individual.
- Children: Number of children the individual has.
- Education: The educational background of the individual.
- Occupation: The occupation of the individual.
- Home Owner: Whether the individual owns a home.
- Cars: Number of cars owned by the individual.
- Commute Distance: The distance the individual commutes.
- Region: The region where the individual resides.
- Age: Age of the individual.
- Purchased Bike: Whether the individual purchased a bike (target variable).
These columns provide a range of demographic information about each individual in the dataset, which will be used to analyze patterns, correlations, and make predictions regarding bike purchases.
The pivot table analysis focused on the following variables and their relationships within the dataset:
Count of Purchased Bike and Age Bracket: This analysis examines the count of purchased bikes based on different age brackets. It provides insights into age groups that are more likely to buy bikes.
Commute Distance and Count of Purchased Bike: This analysis explores the relationship between commute distance and the count of purchased bikes. It helps understand whether individuals with different commute distances exhibit varying bike purchase behavior.
Average Income and Gender: This analysis focuses on the average income based on gender. It allows for a comparison of income levels between males and females in the dataset.
The dashboard includes the following components:
Slicers of Education, Marital Status, and Region: These slicers allow users to filter the data based on education, marital status, and region. Users can select specific options within each slicer to refine the data displayed in the charts and other components of the dashboard.
Charts: Average Income per Purchase: This chart displays the average income for each purchase category. It provides insights into how income levels correlate with bike purchases. Users can observe any trends or differences in average income based on different purchase categories.
Age Brackets with Purchased Bike: This component presents a bar chart or a line chart that shows the distribution of purchased bikes across different age brackets. It helps identify age groups that are more likely to make bike purchases. Users can analyze the chart to understand the age demographics of bike buyers.
Customer Commute with Count of Purchased Bike: This component showcases the relationship between customer commute distance and the count of purchased bikes. It could be presented as a scatter plot or a bar chart, where the x-axis represents commute distance and the y-axis represents the count of purchased bikes. This chart enables users to explore whether commute distance influences bike purchase behavior.
These dashboard components provide a comprehensive overview of the data, allowing users to explore relationships between variables and gain valuable insights into bike purchases based on education, marital status, region, income, age, and commute distance.