This README document provides an overview of the Kenya Food Prices dataset and how to use it in Power BI for data analysis and visualization. The dataset contains information about food prices in Kenya, which can be valuable for analyzing trends, making informed decisions, and understanding the food market in Kenya.

• Dataset Name: Kenya-Food Prices Dataset

• Data Source: https://data.humdata.org/dataset/wfp-food-prices-for-kenya)https://data.humdata.org/dataset/wfp-food-prices-for-kenya

This pie chart visually represents the distribution of categories within different commodities using the dataset provided. Each slice of the pie corresponds to a specific commodity, and its size reflects the count of categories associated with that commodity. The legend displays the names of the commodities, while the values within the chart represent the number of categories within each commodity. This visualization allows for a quick and intuitive understanding of how categories are distributed across various commodities in the dataset.

This clustered bar chart illustrates the distribution of prices by their respective price types and units, utilizing the dataset provided. The y-axis represents different price types, while the x-axis displays the count of prices. Each cluster of bars corresponds to a specific unit, visually demonstrating how prices are distributed across different price types and units in the dataset. This visualization allows for a clear comparison of price counts, helping to identify patterns and trends in the data related to price types and units

This map visualization illustrates the geographical distribution of prices and their relationship with the first commodity within each category using the provided dataset. The map is enriched with data points plotted at specific latitude and longitude coordinates. Each data point is represented by a bubble, with its size indicating the count of prices associated with a particular location. The legend showcases the different categories, latitude, and longitude coordinates, providing valuable context for the data. Additionally, hovering over each bubble reveals a tooltip that displays the first commodity in that category, enhancing the depth of insight into the dataset's spatial patterns and associations. This map offers a geographic perspective on how prices and commodities are distributed across different categories.