Vizly: AI-powered data analysis

Try for yourself

Exploratory Data Analysis of Income, Gender, Region, and Home Ownership

HANDS ON 2.xlsx

Missing file

analyze this dataset for me

Dataset Overview

The dataset contains the following columns:
  • ID: Unique identifier for each record
  • Marital Status: Marital status of the individual (M for Married, S for Single)
  • Gender: Gender of the individual (M for Male, F for Female)
  • Income: Annual income of the individual
  • Children: Number of children the individual has
  • Education: Education level of the individual
  • Occupation: Occupation of the individual
  • Home Owner: Whether the individual owns a home (Yes or No)
  • Cars: Number of cars owned by the individual
  • Commute Distance: Distance the individual commutes to work
  • Region: Geographic region where the individual resides
  • Age: Age of the individual
  • Bike Owners: Whether the individual owns a bike (Yes or No)
  • Unnamed: 13: Contains some instructions or additional information
  • Unnamed: 14: Contains some numerical values related to the instructions
Let's proceed with some basic analysis and visualizations.

Summary of Analysis

  1. Income Distribution: The distribution of income was visualized using a histogram. The plot shows the frequency of different income levels in the dataset.
  2. Gender Distribution: A count plot was created to show the number of individuals by gender. This helps in understanding the gender composition of the dataset.
  3. Regional Distribution: A count plot was created to show the number of individuals by region. This helps in understanding the geographical distribution of the dataset.
  4. Home Ownership by Region: A count plot was created to show the number of home owners by region. This helps in understanding the distribution of home ownership across different regions.
Feel free to ask any more questions or request further analysis!

Continue the analysis

Additional Analysis Summary

  1. Age Distribution: The distribution of age was visualized using a histogram. The plot shows the frequency of different age groups in the dataset.
  2. Marital Status Distribution: A count plot was created to show the number of individuals by marital status. This helps in understanding the marital status composition of the dataset.
  3. Bike Ownership by Region: A count plot was created to show the number of bike owners by region. This helps in understanding the distribution of bike ownership across different regions.
  4. Income vs. Number of Cars Owned: A scatter plot was created to show the relationship between income and the number of cars owned. This helps in understanding how income levels correlate with car ownership.
Feel free to ask any more questions or request further analysis!

Vizly

AI-powered data analysis

Try for yourself