1. Constructing the Data Set: The data science team at Nutri Mondo started by collecting data from various sources such as government databases, non-profit organizations, and other relevant sources. They focused on gathering information related to food insecurity, demographics, socio-economic factors, and other variables that could potentially impact food insecurity in the target regions. After collecting the data, the team cleaned and organized it by removing any inconsistencies, missing values, and outliers. They then combined the data from different sources into a single, unified data set, ensuring that the variables were properly aligned and formatted. **Steps taken to construct the data set:** - Collecting data from various sources - Cleaning and organizing the data - Combining data into a single, unified data set
Step 2/3
2. Initial Patterns Identified: The data science team identified several initial patterns in the data, such as correlations between food insecurity and socio-economic factors like income, education, and employment. They also observed geographical patterns, with certain regions experiencing higher levels of food insecurity than others. These patterns could indicate that addressing socio-economic factors and focusing on specific regions may be key to tackling food insecurity. **Initial patterns identified:** - Correlations between food insecurity and socio-economic factors - Geographical patterns in food insecurity
Answer
3. Exploring and Preparing the Data: To further explore and prepare the data, the data science team conducted exploratory data analysis (EDA) to gain a deeper understanding of the relationships between variables and identify any potential issues with the data. They used various visualization techniques, such as histograms, scatter plots, and heatmaps, to identify trends and patterns in the data. The team also performed feature engineering to create new variables that could provide additional insights into the problem. They then split the data into training and testing sets to prepare for the modeling phase of the project. **Steps taken to explore and prepare the data:** - Conducting exploratory data analysis (EDA) - Using visualization techniques to identify trends and patterns - Performing feature engineering to create new variables - Splitting the data into training and testing sets In conclusion, the data science team at Nutri Mondo has taken a systematic approach to constructing, exploring, and preparing the data set for the food insecurity project. By identifying initial patterns and conducting thorough data analysis, the team is well-prepared to move forward with the modeling phase and ultimately develop effective solutions to address food insecurity.
