Last Updated on May 3, 2021


ACM Student Chapters provide unique opportunities for networking, mentoring and bonding

over common interests. It offers a range of resources and benefits for computing

professionals and students to help them shape the future of computing. And this website witnesses the same, it's a static website for ACM-KCCITM.

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A Review On Weather Forecasting Techniques Using Machine Learning

Last Updated on May 3, 2021


Weather depicts the atmospheric conditions of a particular place at a particular time. The basic weather elements comprise of temperature, wind, pressure, cloudiness and humidity. Every day, the Meteorological Department prepares weather maps for the upcoming day with the help of the data obtained from various weather stations around the world. Weather forecasts help in taking measures in advance in case of the probability of bad weather and in planning your day ahead.


Different instruments are used to measure various weather elements like, a thermometer is used to measure the temperature, whereas, a barometer is used to measure pressure. Similarly, a wind vane is used to find the direction of wind and a rain gauge is used to measure the amount of rainfall. Thus, with the help of the data collected through these instruments we get the weather forecast in the form of weather charts.


In order to decrease so much manual labour, these weather forecasting techniques are now getting replaced with machine learning models that can predict future weather quite accurately with the help of previously collected data. In this report, we are discussing some of the weather forecasting techniques that are most-likely to be used in order to get accurate weather predictions result. Herein we are comparing the results of the various models, just to get the best results.


Keywords: Weather Forecasting, ARIMA, Holt Linear, Holt Winter, Stationarity, Dickey- Fuller


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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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Hospital Readmission

Last Updated on May 3, 2021


Hospital readmissions of diabetic patients are expensive as hospitals face penalties if their readmission rate is higher than expected and reflects the inadequacies in health care system. For these reasons, it is important for the hospitals to improve focus on reducing readmission rates.

We have to Identify the key factors that will influence readmission for diabetes and to predict the probability of patient readmission.

A leading hospital in the US is suddenly seeing increase in the patient readmission in less than 30 days. This is serious concern for the hospital as it may indicate insufficient treatment or diagnosis when the patient was admitted first and later released under clean bill of health. Hence it is in Hospital’s interest to support their diagnosis by a better predictive model which we are going to build.

Here the objective is: Classify the patients treated by this hospital into two primary categories:

·      Readmitted within 30 days

·      Not readmitted

The dataset chosen is that available on the UCI website which contains the patient data for the past 10 years for 130 hospitals. The code has been written in Python using different libraries like scikit-learn, seaborn, matplotlib etc. Different machine learning techniques for classification and regression like Logistic regression, Random forest etc .have been used to achieve the objective.

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Govindasamy Bala - Iisc Bangalore | A Significant Step In Understanding Climate Change

Last Updated on May 3, 2021


We are currently living in what is considered as the Anthropocene, an age in which human activities have a significant impact on the planet. Perhaps the most serious damage inflicted by humans has been on its climate. Climate change, driven by an increase in the average surface temperature of Earth, results from a surge in radiative forcing—the difference between the energy received by Earth and the energy radiated back to space. The radiative forcing agents of the industrial era include greenhouse gases such as carbon dioxide (CO2) and methane, which trap the longwave radiation emitted by our planet. To measure how effective a forcing agent is in causing Earth’s climate to change, researchers use the concept of efficacy, defined as the ratio of global temperature change due to that particular forcing agent to the temperature change caused by CO2 for the same radiative forcing value.

In a new study, GovindasamyBala—one of India’s most well-known climate scientists—and his student, AngshumanModak, addressed the issue of the efficacy of the incident solar radiation relative to CO2.* Using a modelling approach, they considered climate system responses during three different time periods: a week, four months and a century.

“What we found was that the Sun is less effective than CO2 in causing climate change,” says Bala. In fact, the study shows that solar forcing is only 80% as effective as CO2 forcing. “This means that for the same radiative forcing, if CO2 causes 1 °C warming, the Sun causes only 0.8 °C warming,” he continues. This finding, Bala argues, is crucial not just for our understanding of the mechanisms of climate change but also for the formulation of more effective climate change policies. 

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Black Friday Sales Prediction

Last Updated on May 3, 2021


Black Friday Sales Prediction is simply a prediction of sales of different products. Main goal of this project is to find out customer purchase behaviour against various products of different categories. I have  purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stay in current city), product details (product id and product category) and Total purchase amount from last month. Based on this data we will predict sales.

For simplicity i divided my projects into small parts-

  1. Data Collection :- I collected data from 'Anylitical Vidhya' as a CSV file. We have two CSV file one is train data which is used for training the data and other is test data which is used for prediction based on training of model.
  2. Import Libraries:- I import differnt sklearn package for algorithm and different tasks.
  3. Reading data:- i read the data using pandas 'read csv()' function.
  4. Data Preprocessing -: In this part i first found missing values then i remove a column or imputed some value (mean,mode,median) According to the amount of data missing for a particular column.

I checked the unique value in each column. Then i did label encoding to convert all string types data to integer value. I find out correlation matrix which shows the correlation between columns to each other.

Then i split the data. Then i create a regression model. I trained that regression model using Random Forest Algorithm .I feed training dataset to model using random forest algorithm. After creating model i did similiar data preprocessing to test dataset . And then i feed test dataset to trained regression model which predict the values of this test dataset. And then i found accuracy of this model using actual target value which is given in training dataset. and predict target value which we predict from test dataset.

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