This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.
From the data set in the (.csv) File We can find several variables, some of them are independent (several medical predictor variables) and only one target dependent variable (Outcome).
• I downloaded and opened my dataset and attempted to understand the type of analysis expected.
Pandas: Data analysis and manipulation library for working with structured data using Data Frame and Series.
NumPy: Numerical computing library supporting large, multi-dimensional arrays and matrices, with high-level mathematical functions.
Seaborn: Statistical data visualization library for creating attractive and informative graphics, based on Matplotlib.
Matplotlib: Comprehensive plotting library providing interface for creating various plots like line, scatter, bar, and histograms.
Train Test Split: Technique for splitting data into training and testing sets to assess model performance.
outcome using the logistic function.
Accuracy: Metric measuring the proportion of correctly classified instances in a classification model.
Sklearn: Python's Scikit-learn, a powerful machine learning library providing tools for data analysis and model building.
In conclusion, this project demonstrates the feasibility of using machine learning techniques to predict diabetes in female individuals of Pima Indian heritage. The trained model can serve as a valuable tool for healthcare professionals in early diagnosis and intervention, ultimately improving patient outcomes.