In this internship, you will use most important data science concepts like logistic regression, and tools like numpy, pandas and sci-kit learn in Python to develop a model which predicts if someone is likely to develop diabetes or not.

It is important to learn about these technique for real world because,

Diabetes is a chronic condition that affects the way the body processes blood sugar, also known as blood glucose. In people with diabetes, the body either doesn't produce enough insulin to process the glucose in the blood, or the cells in the body are resistant to the effects of insulin. This can cause a variety of symptoms, including increased thirst, frequent urination, and fatigue. Over time, uncontrolled diabetes can lead to serious health complications, such as heart disease, nerve damage, and kidney disease.

If we can detect Diabetes early and starts it treatment than it can help prevent or delay the onset of diabetes-related complications, people with diabetes can manage their condition by eating a healthy diet, exercising regularly, and taking medications as prescribed.

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About the techniques you will learn in this internship,

Logistics Regression - It is a type of statistical analysis used to predict the outcome of a binary dependent variable, based on one or more independent variables. In logistic regression, the dependent variable is always binary, meaning it can have only two possible values, such as "yes" or "no." The independent variables can be either binary or continuous. The goal is to find the best fitting model that describes the relationship between the dependent and independent variables. This is typically done using a maximum likelihood estimation, which estimates the parameters of the model that are most likely to have generated the observed data.

Numpy - It is a popular library for scientific computing in Python. It provides tools for working with arrays of data, and it is particularly useful for working with large and complex datasets. NumPy provides many features that are not available in the core Python language, such as support for large, multidimensional arrays, support for mathematical operations on those arrays, and tools for reading and writing array data to and from disk.

Pandas - It is a popular library for data manipulation and analysis in Python. It provides tools for working with tabular data, such as data stored in a table with rows and columns. Pandas allows for easy manipulation of the data, such as filtering, aggregation, and transformation, and it also provides tools for reading and writing data to and from a variety of formats, such as CSV, Excel, and SQL databases.

Scikit-learn - It is a popular library for machine learning in Python. It provides a wide range of algorithms for supervised and unsupervised learning, including classification, regression, clustering, and dimensionality reduction. Scikit-learn is built on top of NumPy, SciPy, and Matplotlib, and it integrates seamlessly with those libraries to provide a single, cohesive environment for data manipulation, analysis, and modeling. Scikit-learn is widely used in industry and academia, and it is a valuable tool for anyone interested in using machine learning to gain insights from data