# Data Science Internship: Diabetes Prediction

Rated

5

(Out of 5)

Orientation on:

100,000+ Participants

have chosen our platform to learn coding.

1999
2500

Rated

5

(Out of 5)

00
Days
00
Hrs
00
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00
Sec

## Data Science Internship: Diabetes Prediction

Starts From:
Tutorials:
10
Duration:
4 weeks
Starts From:
Duration:
4 weeks
Eligibiliity:
Any Graduate
Early Bird Price:
1999
2500
Orientation:

## About Internship

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.

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

# Skills you can learn in this course

Python
numpy
pandas
Data Science
Machine Learning

## Instructors

Aneeq

Aneeq Dholakia has trained thousands of students from tier 2/3/4 colleges and helped them achieve their dream companies. Aneeq excels at training for competitive programs like TCS, CodeVita, HackWithInfy and product companies like JP Morgan, ServiceNow, Adobe, Amazon and more.

Shubham

Shubham Lal, is a Software Developer (SDE-2 ) at Microsoft who has mentored 5,000+ students and has been invited as a speaker in 20+ events. As an instructor, Shubham has worked with leading EdTech's in India.

## Verifiable Certificate

You can share your Internship Certificate in the certifications section of your LinkedIn profile, on printed resume or other documents.

All certificate images are for illustrative purposes only and may be subject to change at the discretion of Edyst

## You Will Get :-

Joining Letter
Internship Report
(as per AICTE guidelines)
Certificate of Completion
Guidance on uploading project to Github
Guidance on how to explain project to interviewer
Guidance on how to put project in resume
Doubt clearing session
Exclusive guidance from industry mentor
Improvise LinkedIn Profile
*Orientation -

## Key Programme Takeways:-

Increase interview call, internship experience letter + internship joining letter + internship evaluation report (as per AICTE guidelines)
You’ll learn the easiest way to get rid of problems comes in data cleaning
Plan and implement appropriate machine learning techniques in a simulated environments
Leverage insights from the data to reach conclusions and showcase your enhanced skill set on LinkedIn and GitHub profile.
Maximize engagement on new projects and work with CSE students from different colleges.

## Why Edyst?

Other Platforms
Internship Evaluation Report
AICTE guidelines based internship evaluation report
No report
Curriculum
Designed by an industrial trainer working as SDE-2 at Microsoft with 5+ experience
Designed by a non-industrial trainer
Joining Letter & Experience Letter
Internship joining letter & experience letter adds credibility to your documents
No joining letter is provided
Lifetime Association
Lifetime association with Edyst - #1 Careers Community in India
Associated with a non credible institution
Credibility of Faculty
Learn from experts from top tech. globally famous product firms
Faculty working in less known IT service firm
Placement
100,000+ students have crack off campus interviews using our courses - check our students video testimonials below
No placement / reviews

## Career Services

Building an Impressive Project
Building an Impressive GitHub Profile
Internship Experience
Project Explaining Guidance

## Harika

Placed in
DBS
-
7 LPA

I feel the best thing about edyst is its company-specific guidance and the huge problem sets covering up almost all the concepts from beginner to advanced concepts, it helped me a lot for my placement preparation

## Sriram

Placed in
Seawise Capital
-
8 LPA

When I joined the Edyst courses I received personalized mentoring on how to crack coding rounds of different companies. Through a combination of coding skills and great projects, I received multiple offers above 6+ lakhs per annum. Finally I joined for 8+ Lakhs package. Thanks for all the support, from Edyst Team.

## Sakila

Placed in
Adobe
-
12 LPA

I started practising on Edyst platform since my 3rd year of college focused on placements & always liked the way they helped us when we were stuck at a particular problem.
Thank you, Edyst for all the assistance and amazing support!

## Vaishnavi

Placed in
ServiceNow
-
24 LPA

Edyst's training style completely resonated with me. I approached programming as more than a subject. Thanks to Edyst team for the guidance!

## Raviteja

Placed in
EPAM
-
6 LPA+

Edyst platform has helped me enhance my skills and problem solving ability. Some of the tips shared by them helped me to clear the coding round & interview effectively.
Thank you for this opportunity, Edyst!

## Dileep

Placed in
TCS Digital
-
7 LPA+

Being a mechanical student and getting into an IT company is very tough. One of the main reason I could able to crack TCS CodeVita is because of Edyst.
Aneeq sir, your doubt clearing sessions, daily assignments & incredible mentors support really brushed up my skills.

## Manikanta Javvadi

Placed in
Phenom People
-
15 LPA

Daily coding challenge and the doubt session helped me in staying consistent. Also, the leadership board kept me motivated. Edyst gave me the best guiding materials for all the cohorts I joined. I like everything done by Edyst for my success.

## Jayaram Majeti

Placed in
Cognizant
-
6.75 LPA

I joined Edust because the platform has wide range of practice questions. Also, there was mentors support throughout the day to help students when they get stuck. All the mentors were very friendly and helpful, the chat support feature of Edyst is best.

## Ganesh Gaikwad

Placed in
Nice
-
6 LPA

Edyst platform is the main reason for my placement. Edyst helped in developing my logic building skills. The trainers were absolutely fantastic. Their teaching skills are outstanding. Also the coding questions on the platform were very much useful for developing logic and understanding for any student.

## Abhishek Surya

Placed in
Wipro
-
6.5 LPA

I liked the assessment facility offered by Edyst, using this feature I prepared for all the competitive companies where Computer Science graduates get placement. As a CS background student Edyst platform helped me a lot to furnish my coding skills. It helped me to perform upto my true potential during my placement exams.

*Orientation -

## Our Alumni are Working at

50+ Companies

Note: All product and company names are trademarks or registered trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.

Price:
1999
2500
00
Days
00
Hrs
00
Min
00
Sec

## Harika

Placed in
DBS
-
7 LPA

I feel the best thing about edyst is its company-specific guidance and the huge problem sets covering up almost all the concepts from beginner to advanced concepts, it helped me a lot for my placement preparation

## Sriram

Placed in
Seawise Capital
-
8 LPA

When I joined the Edyst courses I received personalized mentoring on how to crack coding rounds of different companies. Through a combination of coding skills and great projects, I received multiple offers above 6+ lakhs per annum. Finally I joined for 8+ Lakhs package. Thanks for all the support, from Edyst Team.

## Sakila

Placed in
Adobe
-
12 LPA

I started practising on Edyst platform since my 3rd year of college focused on placements & always liked the way they helped us when we were stuck at a particular problem.
Thank you, Edyst for all the assistance and amazing support!

## Vaishnavi

Placed in
ServiceNow
-
24 LPA

Edyst's training style completely resonated with me. I approached programming as more than a subject. Thanks to Edyst team for the guidance!

## Raviteja

Placed in
EPAM
-
6 LPA+

Edyst platform has helped me enhance my skills and problem solving ability. Some of the tips shared by them helped me to clear the coding round & interview effectively.
Thank you for this opportunity, Edyst!

## Dileep

Placed in
TCS Digital
-
7 LPA+

Being a mechanical student and getting into an IT company is very tough. One of the main reason I could able to crack TCS CodeVita is because of Edyst.
Aneeq sir, your doubt clearing sessions, daily assignments & incredible mentors support really brushed up my skills.

## Manikanta Javvadi

Placed in
Phenom People
-
15 LPA

Daily coding challenge and the doubt session helped me in staying consistent. Also, the leadership board kept me motivated. Edyst gave me the best guiding materials for all the cohorts I joined. I like everything done by Edyst for my success.

## Jayaram Majeti

Placed in
Cognizant
-
6.75 LPA

I joined Edust because the platform has wide range of practice questions. Also, there was mentors support throughout the day to help students when they get stuck. All the mentors were very friendly and helpful, the chat support feature of Edyst is best.

## Ganesh Gaikwad

Placed in
Nice
-
6 LPA

Edyst platform is the main reason for my placement. Edyst helped in developing my logic building skills. The trainers were absolutely fantastic. Their teaching skills are outstanding. Also the coding questions on the platform were very much useful for developing logic and understanding for any student.

## Abhishek Surya

Placed in
Wipro
-
6.5 LPA

I liked the assessment facility offered by Edyst, using this feature I prepared for all the competitive companies where Computer Science graduates get placement. As a CS background student Edyst platform helped me a lot to furnish my coding skills. It helped me to perform upto my true potential during my placement exams.

*Orientation -

## Course Syllabus

Revision of Python:

1. Data Types in Python
2. Arithmetic operations
3. Loops and Iterations
4. Comprehensions
5. Functions in Python
6. Map, Filter, and Reduce Functions
7. Lists
8. Tuples
9. Sets
10. Dictionaries

Numerical Python (NumPy)

1. Introduction to NumPy
2. Basics of NumPy
3. Operations over 1-D Arrays
4. Multidimensional Arrays
5. Creating NumPy Arrays
6. Mathematical Operations on NumPy
7. Mathematical Operations on NumPy II
8. Computational Times in NumPy vs Python Lists

Pandas

1. Introduction to Pandas
2. Basics of Pandas
3. Pandas - Rows and Columns
4. Describing Data
5. Indexing and Slicing
6. Operations on DataFrames
7. Groupby and Aggregate Functions
8. Merging DataFrames
9. Pivot Tables

Exploratory Data Analysis

1. Introduction to Univariate Analysis
2. Categorical Ordered/Unordered Univariate Analysis
3. Statistical on Numerical Features
4. Correlation vs Causation
5. Multivariate Analysis

Data Visualisation (Matplotlib)

1. Introduction to Matplotlib
2. The Necessity of Data Visualisation
3. Visualisations - Some Examples
4. Facts and Dimension
5. Bar Graph
6. Scatter Plot
7. Line Graph and Histogram
8. Box Plot
9. Subplots
10. Choosing Plot Types

Univariate logistic regression

1. Introduction
2. Binary classification
3. Sigmoid curve
4. Finding the best fir sigmoid curve
5. Odds and log Odds

Multivariate logistic regression

1. Introduction
2. Building the model
3. Feature elimination using RFE
4. Confusion matrix and accuracy
5. Manual feature elimination

Multivariate logistic regression - Model Evaluation

1. Metrics beyond accuracy - sensitivity and specificity
2. Understanding ROC Curve
3. Finding the optimal Threshold
4. Model evaluation metrics
5. Precision and recall
6. Making predictions

Nuances of Logistic regression

1. Sample selection
2. segmentation
3. variable transformation

Problem Solving, Presentation and Story Telling

1. Diabetes Prediction Case Study
2. Prediction vs Projection
3. EDA
4. Model Building
5. Assessing the Model
6. Interpreting the results

## Frequently Asked Questions

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## Early Bird Offer is for limited time period only

Starts From:
Duration:
4 weeks
Eligibility:
Any Graduate
Early Bird Price:
1999
2500
Orientation:
*Early Bird Offer will be applied automatically at the time of payment