Sentiment Detector Bot

Last Updated on May 3, 2021


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Determination Of A Person’S Health

Last Updated on May 3, 2021


Determination of person’s health

The project was built with the intend of helping the society. It has been calculated that approx. 1.9 billion people die due to health-related problems every year. This rate is very high, and the disease is easily preventable

The project has been made with the help of Data Analysis and Machine Learning using Python with a GUI output page. In this project, the machine will analyse the already present data first and then conclude upon a person’s health on his/her given factors.

In this project, gender and either height or weight will be given to the machine. If the height is given then the weight will be predicted and vice-versa. Through these predictions the machine will tell us about the health of a person.

The main goal is to help the society for its betterment as far as health is concerned.

The data set used is from UCI repository. It includes four attributes-

1.     Gender

2.     Height

3.     Weight

4.     Index

The machine will be trained in these aspects to determine a person’s health or weight and the category it will lie in.

The categories are-

1.     0 – Underweight

2.     1 – Normal weight

3.     2 – Healthy

4.     3 – Over weight

5.     4 – Obesity

The methods followed in chronological form are-

1.     Loading dataset (using pandas library)

2.     Dataset cleaning (using pandas and numpy libraries)

3.     Dataset pre-processing

4.     Data visualization (using seaborn, matplotlib and matplotlib.pyplot libraries)

4.1  Univariate analysis

4.2  Bivariate analysis

5.     Correlation matrix 

The machine learning algorithms applied were-

1.     Linear Regression

2.     Logistic Regression

3.     KNN Classifier

4.     Decision Tree Classifier

5.     Random Forest Classifier

Random Forest Classifier gave highest accuracy of about 95% while logistic regression gave the leas with about 76%.

The user in the GUI page will be asked:

1. Full name

2. Gender

3. Whether they know their height or weight

4. Their height or weight

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Emotional Analysis Based Content Recommendation System

Last Updated on May 3, 2021


As the saying goes, “We are what we see”; the content we see may have an adverse effect on our behavior sometimes. Especially in a country like India, where numerous films and TV series are highly prominent, there are great chances of watching explicit or disturbing content randomly. This may have adverse effects on behavior of people, especially children. And we also know “Prevention is better than cure”. Preventing inappropriate content from going online can be more effective than banning them after release.

To achieve this, we aim to create a content filtering and recommendation system that either recommends a film or TV series or alerts a user with a warning message saying it’s not recommended to watch. Netflix or any other Over-the-top (OTT) platforms perform a filtering process before they buy digital rights for any content. This is where our tool comes handy. It detects absurd or hard emotion inducing content with the help of human emotions. Through this project we aim to create a content detector based on human emotion recognition. We will project scenes to test audience and capture their live emotions.

Then we use “Facebook Deep Face”, a pre-defined CNN based face recognition and facial emotion analysis model to identify faces and analyze their emotions. We use “Deep Learning” methods to recognize facial expressions and then make use of Circumplex Model proposed by James Russell to classify emotions based on arousal and valence values. Based on majority emotion that is projected by audience we would either recommend or not recommend the content for going on-air. This system prevents inappropriate content from going on-air

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Last Updated on May 3, 2021


Augmented Reality Circuit Visualizer and Solver.

Categories Won

TartanHacks Grand Prize

Facebook Company Prize

Attempted Prize Categories

Duolingo’s Social Impact Prize (Educational)

GoDaddy’s Social Impact Prize (“Best app that improves STEM education”)


Long hours spent on ECE problem sets and frustration visualizing convoluted circuits caused these four CMU undergrads to create a circuit visualization system that would also help them solve circuits. A member of the team is currently in the intro ECE course: "Well it's not bad, I guess." - Team Member

What it does

ResistAR is an Augmented Reality Circuit Visualizer and Solver. A user can place down circuit elements in parallel and series configurations and ResistAR will solve the current through and voltage across each element of the circuit. It gives the user an easy way to see (sharp) the circuit.

How we built it

We first began with 3D printed chassis for the VuMark targets. These targets are identified and parsed by the program and cross checked against our cloud database on Vuforia. We then created 3D, textured, models in Blender that will hover over the VuMark targets. We then wrote the code in Unity that will calculate voltage and current values using concepts from vector calculus and matrix algebra.

Challenges we ran into

The math was very difficult and attempting to rush a 3D printed design was also difficult but there was a rush because 3D printing would be a very time consuming process. Thus we also had to create a lot of our latter designs around the already 3D printed parts. VuMarks were also difficult to create. VuMarks must be very easily distinguishable from each other and non-symmetric along any axis, and therefore took a while to get finely tuned and calibrated. Finally the math was a very difficult thing to visualize. We had to go from 3D space to 2D space and there were some difficulties with projections. The coders did end up writing relatively bug-free code, but not before a long, arduous thinking process.

Accomplishments that we're proud of

The two logic/algorithm gods that we had on our team solved an extremely complex math problem very quickly. Also our 3D printed parts are actually fire though. Just saying.

What we learned

Two 5 hour energies in 72 hours is actually not as bad an idea as some might think. Math is hard.

What's next for ResistAR

Norton and Thevenin Equivalents. Yikes.

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E Commerce Website

Last Updated on May 3, 2021


The primary goal of an e-commerce site is to sell goods and services online. Online shopping is a form of electronic shopping store where the buyer is directly online to the seller’s computer usually via the internet. A person sitting on his chair in front of a computer can access all the facilities of the Internet to buy or sell the products. Online Shopping System helps in buying of goods, products and services online by choosing the listed products from website(E-Commerce site). 

The Shopping cart is mainly useful for who haven’t time to go to shopping. Shopping cart is a very important feature used in e-commerce to assist people making purchases online. The sale and purchase transaction is completed electronically and interactively in real-time. User can login into eCommerce website, once he logged in then automatically one shopping cart will be created, once user select an item it will add to cart. In case user thinks the selected item is not useful for him, then he can delete that item form the cart.

Shopping Cart feature allows online shopping customers to “place” items in the cart. It Decreases the cost of creating, processing, distributing, storing and retrieving paper-based information. Expands the marketplace to national and international markets.  Upon “checkout” the software calculates as total for the order including shipping and handling postage, packing and taxes, if applicable. Reduces the time between the outlay of capital and the receipt of products and services.

The proposed system helps in building a website to buy, sell products or goods online using internet connection. Enables consumers to shop or do other transactions 24 hours a day, all year round from almost any location. It can be accessed over the Internet. 

Purchasing of goods online, user can choose different products based on categories , online payments , delivery services and hence covering the disadvantages of the existing system and making the buying easier and helping the vendors to reach wider market. It Provides consumers with more choices. Customer can purchase Products Online.

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Project - Mercedes-Benz Greener Manufacturing

Last Updated on May 3, 2021



Reduce the time a Mercedes-Benz spends on the test bench.

Problem Statement Scenario:

Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations. These include the passenger safety cell with the crumple zone, the airbag, and intelligent assistance systems. Mercedes-Benz applies for nearly 2000 patents per year, making the brand the European leader among premium carmakers. Mercedes-Benz cars are leaders in the premium car industry. With a huge selection of features and options, customers can choose the customized Mercedes-Benz of their dreams.

To ensure the safety and reliability of every unique car configuration before they hit the road, Daimler’s engineers have developed a robust testing system. As one of the world’s biggest manufacturers of premium cars, safety and efficiency are paramount on Daimler’s production lines. However, optimizing the speed of their testing system for many possible feature combinations is complex and time-consuming without a powerful algorithmic approach.

You are required to reduce the time that cars spend on the test bench. Others will work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Daimler’s standards.

I have done Data exploration, checking for Missing values and Outliers. Treat the outliers. Applied Label Encoding on categorical variables. I have scaled the data. Applied PCA to reduce the dimension of data but no effect of it on the result. In the prediction, I used Random Forest, KNN, and XGBoost modelling. In all of them, XGBoost has given good result.

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