Exploring Numerical Data in the 'bike_buyers' Dataset
May 21, 2024, 10:25 AM
Analyzed my data set for me
The dataset 'bike_buyers' was successfully loaded. It contains 1026 rows and 14 columns, including information on marital status, gender, income, education, occupation, home ownership, number of cars, commute distance, region, age, and whether the individual purchased a bike.
The first few rows of the 'bike_buyers' dataset were displayed. The columns include:
ID
Marital Status
Gender
Income
Children
Education
Occupation
Home Owner
Cars
Commute Distance
Region
Age
Purchased Bike
Column1
Summary statistics for the numerical columns in the 'bike_buyers' dataset were generated:
Income: Ranges from 10,000 to 170,000, with a mean of 56,209.
Children: Ranges from 0 to 5, with a mean of 1.89.
Cars: Ranges from 0 to 4, with a mean of 1.44.
Age: Ranges from 25 to 89, with a mean of 44.14.
Column1: Contains large values with a mean of approximately 1.85 million, but only 8 entries.
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Missing Values Check: The dataset has no missing values except for 'Column1', which has 1018 missing values.
Data Cleaning:
Converted 'Income' to numeric.
Mapped 'Purchased Bike' to binary.
Dropped 'Column1' due to irrelevance and missing values.
One-Hot Encoding: Applied to categorical columns.
Correlation Matrix: Visualized to understand relationships between variables.
Visualize this for me
The correlation matrix has been visualized using a heatmap.
The plot uses the 'coolwarm' colormap and displays correlation coefficients with two decimal points.
The heatmap provides a clear, visual representation of the relationships between different variables.
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Summary of Exploratory Data Analysis (EDA)
We conducted an exploratory data analysis (EDA) to understand the distribution of key variables and their relationships within the dataset. Here are the key findings:
Income Distribution: Visualized the distribution of income using a histogram.
Age Distribution: Visualized the distribution of age using a histogram.
Income vs. Number of Cars Owned: Explored the relationship between income and the number of cars owned using a scatter plot with a trendline.
Age vs. Purchased Bike: Examined the relationship between age and the likelihood of purchasing a bike using a scatter plot with a trendline.
These visualizations help in understanding the underlying patterns and relationships in the data, which can be useful for further analysis or model building.
Can you analyze the impact of education on bike purchasing
Impact of Education on Bike Purchasing
A bar plot has been generated to visualize the relationship between education levels and bike purchasing.
The plot displays the count of bikes purchased across different education levels.
The results will help in understanding how education influences the decision to purchase a bike.
can you carry out a segmentation analysis for me
Segmentation Analysis
To carry out a segmentation analysis, we will group the data based on certain criteria and analyze the characteristics of each segment. This will help in identifying distinct groups within the data and understanding their unique attributes.
Let's proceed with the segmentation analysis.
Segmentation Analysis Using K-Means Clustering
We have performed the initial steps for segmentation analysis using K-Means clustering. The elbow method was used to determine the optimal number of clusters. The elbow graph has been generated to visualize the Within-Cluster Sum of Squares (WCSS) for different numbers of clusters.
Next, we will proceed with clustering the data based on the optimal number of clusters and analyze the characteristics of each segment.
Segmentation Analysis Using K-Means Clustering
Key steps and findings:
Initial Setup: Installed necessary libraries and prepared the dataset by selecting relevant features (Income, Age, Cars, Children), converting categorical data, handling missing values, and standardizing the features.
Elbow Method: Used the elbow method to determine the optimal number of clusters. An elbow graph was generated to visualize the Within-Cluster Sum of Squares (WCSS).
Clustering: Applied K-Means clustering with 4 clusters (as an example).