Talasila has been passionate about technology from a young age, He is fascinated by the immense potential of data science and machine learning to transform industries and solve complex challenges. Talasila aspires to develop innovative solutions that positively impact people's lives, whether that's optimizing business processes, advancing healthcare systems, or tackling environmental challenges.

Poster Abstract

Introduction:

The presentation aims to perform analysis on the Google Play Store apps dataset to identify trends and extract valuable insights. The Google Play Store is a huge repository containing diverse categories such as ratings, reviews, and other important data in over 10,000 rows and 13 columns. This project seeks to explore this dataset and visualize important patterns using HPCC Systems®.

Objective:

The main objectives of this presentation are to clean, filter and preprocess the dataset utilizing ECL to maintain its quality. Once the dataset is fully prepared, it would be ready to perform various statistical calculations and plot visually appealing and informative plots, graphs, and charts. This visualization will help in a better understanding of the data and would benefit in market dynamics.

Methodology:

The process begins by spraying of the Google Play Store apps Dataset onto the ECL IDE. Once the dataset is available in the IDE, it should be cleaned, i.e. (without having any null values (NaN)) and pre-processed using data manipulation functions such as SORT, GROUP, AVG, FILTER, etc. Once the dataset is pre-processed, we need to identify the relevant type of visualizations needed such as bar chart, line graph, scatter plot, or others. Once we have identified the type of visualizations, we can select the appropriate visualization library available in ECL. Using the "visualize" function of the selected library to create the desired chart or graph and also set the dataset columns as the X and Y axis values to plot the data accurately. Further, customize the appearance and layout of the chart or graph using the available options of the selected library, if required. Finally, we can save the visualization output in a desired format or file type to ensure that it can be accessed later or shared with others effortlessly. The final outcomes of this project can be used as inputs to analysis of the data, that helps in understanding app characteristics, user preferences, and market trends.

Presentation

In this Video Recording, Talasila provides a tour and explanation of his poster content.

Data Analysis and Visualization on Google Play Store Apps Dataset using ECL

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