Malnourishment-oneAPI

by Ashwin Satish, 2041014 3BCA A, Central Campus

Inspiration

Malnourishment can have a significant impact on physical and mental health, leading to stunted growth, weakened immune system, cognitive impairment, and even death. To understand further, I have chosen this domain/topic.

Steps involved in the building

✅ Import relevant and related libraries

✅ Understanding and perceiving important information from the data

✅ Creating correlations and visualizing it

✅ Testing several models and find the best model from it

✅ Training the model using Intel oneDAL to get better results and faster computation (Intel oneAPI Data Analytics Library [oneDAL] )

From that, we see

🌟 SVM (Support Vector Machine) : 48.39%

🌟 Logistic Regression : 45.16%

🌟 RF : 58.06%

🌟 XGBoost : 48.39%

🌟 Decision Tree : 48.39%

🌟 K-Nearest Neighbours : 22.58%

Learning Output -> OneAPI

👉 Understood key features of oneDAL by Intel like:
.🎯Scalability and performance optimizations for big data analytics.
.🎯Integration with popular programming languages such as C++, Python, and Java.
.🎯Built-in support for common machine learning algorithms, including linear regression, decision trees, k-means clustering, and support vector machines.
.🎯Interoperability with other Intel software tools and libraries for optimized performance, such as the Intel Math Kernel Library (MKL) and the Intel Distribution for Python.

👉Understanding of the data: Learning how to preprocess and clean the data, as well as how to handle missing values and categorical variables.I conducted exploratory data analysis to gain insights into the relationships between the variables which helped me understand the domain region more based by statistical data and facts.

👉Selection of appropriate algorithms: I learned how to select appropriate machine learning algorithms for the given problem. For example, logistic regression may be useful for binary classification problems, while decision trees may be better suited for multiclass problems.

👉Machine Learning: I learned about different machine learning algorithms and how they can be applied to understand malnourishment that exists and make understand the dataset further

👉Data Analysis: I gained experience in collecting and analyzing large amounts of data, including historical data, to train our machine learning models.

👉Comparison of model performance: I learned how to compare the performance of different models using appropriate statistical tests or visualizations. This can help you choose the best model for the given problem.