Analyzing Open Data to Drive Positive Change in Lima

Start Date: October 16, 2023 | 3 years ago

Challenge Background

Within Peru's Open Data Platform lies a rich tapestry of information spanning various domains and applications. Our mission, as part of the Omdena initiative, is to dive into this expansive dataset with a singular purpose: to extract actionable insights that can drive positive change in the city of Lima and enhance the quality of life for its residents. Lima, a city pulsating with vitality, holds enormous potential for growth and transformation. By meticulously analyzing this diverse data using analytics tools, we aim to unearth valuable knowledge, patterns, and opportunities. Our ultimate vision is to empower city leaders, policymakers, and the people of Lima with the means to make informed decisions that will not only better the city itself but also the lives of its citizens.

The Problem

The availability of Peru's Open Data Platform, which encompasses an extensive range of unstructured datasets spanning diverse domains such as the economy, mining, agriculture, health, and more, presents a unique opportunity and a substantial challenge. The problem at hand is twofold: first, these datasets are currently disorganized and underutilized, hindering their potential to support research and open-source initiatives. Second, there exists an untapped potential for harnessing this data to gain critical insights aimed to be visible to the residents of Lima.

Furthermore, the absence of compelling visualizations, and comprehensive reports that draw connections across multiple datasets poses a barrier to informed decision-making. Our overarching goal is to leverage this wealth of data to address these challenges, fostering a culture of data-driven innovation in Lima. To do so, we need to stimulate local community involvement in generating powerful insights to further our understanding and drive improvements in the city's quality of life.

Goal of the Project

Create a comprehensive index of open datasets about Lima.
Integrate, analyze, and visualize diverse open datasets about Lima.
Create a comprehensible new dataset related to Lima.
Additional (not obligatory): Host results on a website.

Project Timeline

Determining key websites that list datasets about Lima.
Deciding how to organize datasets, limitations, visualizations, and reports.
Deciding the new datasets to create.

Data Collection and Preprocessing:

Starting to create the index of datasets and websites that display them.
Starting to create visualizations and reports using the datasets in the index.
Starting to scrap the data of the new datasets.

Exploratory Data Analysis (EDA):

Improving the index of the database and creating graphs and visual reports. Searching for options to display the results.
Organising the datasets and labelling them.
Conducting the EDA to gain insights into historical price patterns, seasonal variations, and relationships between different commodities.
Displaying the data by using libraries (e.g. Matplotlib, Seaborn, Microsoft Excel, Power Bi, Pingouin, etc. ) to highlight trends and correlations.

Feature Engineering: Select relevant features for prediction, including time-based patterns, economic indicators, and external factors like weather conditions.

Verifying data by testing the data and confirming the organization. Displaying results and reports.

Interactive Web Application: Developing an interactive web application using frameworks like Flask, Streamlit, Medium, or Wordpress.

Implementing visualisations of historical trends, predicted prices, and key insights for users to explore. Creating a presentation for explaining the data.

Creating a presentation for explaining the data. Publishing the data on different social media e.g. Facebook, Linkedin, Instagram, TikTok, etc.

Creating a repository with the data, the processing, the presentation, and the graphs generated on the project.

What you'll learn

Data Analysis, Python, EDA, Visualization, Data understanding

First Omdena Local Chapter Project?

Beginner-friendly, but also welcomes experts

Education-focused

Duration: 4 to 8 weeks

Open-source

Your Benefits

Address a significant real-world problem with your skills

Build your project portfolio

Access paid projects (as an Omdena Top Talent)

Get hired at top organizations

Requirements

Good English

Suitable for AI/ Data Science beginners but also more senior collaborators

Learning mindset

Application Form

Application Closed.