Thesaurus Using Python

Last Updated on May 3, 2021


I have created a JSON file which contains all the words and it's meaning..I have imported get close matches library from difflib Library.The use of get close library is that it displays the words that closely matches the given input.It doesn't matter if the user gives the input in upper case or lower case because the necessary conditions are also included.

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My Rewards - Alexa Skill

Last Updated on May 3, 2021


An Alexa skill to reward kids for good behavior.


After building I decided to purchase an Alexa Show (2nd gen) for comparison. eFamilyBoard has a few nicer features but over all the Alexa is much more powerful and scalable. One heavily used feature was the sticker board on eFamilyBoard that didn't have a comparable skill on Alexa. As a result, I decided to build My Rewards skill to replace it.

What it does

It allows families and teachers to reward kids for good behavior. The user ultimately decides what to do with the rewards. Personally, our kids earn $5 after they've earned 10 total rewards, then they start over. The user can add recipients and give multiple rewards at a time. For example, "Alexa give John 5 stickers" or "Alexa take away 2 stickers from John". And if you don't know what reward type of reward to give or take away you can always simply say "rewards" in the place of the type of reward (e.g. football, sticker, heart, unicorn, truck, cookie, doughnut, etc).

How I built it

I built it with the ASK CLI and Visual Studio Code. I started with the sample hello world app and refactored it to utilize typescript, express, and ngrok to run locally. I also used mocha with chai for unit tests that run and must pass before I can deploy to AWS.

Challenges I ran into

I learned to get stated by taking a course on Udemy but they didn't use typescript and deployed to AWS for every change. That would take FOREVER to debug and build efficiently. I setup a simple express app and use ngrok to route calls to my local machine. This allows me to talk to my Alexa and debug by stepping through the code in VS Code.

Accomplishments that I'm proud of

Project setup, local debugging with typescript, and tests with 90%+ code coverage. Not only does it work for voice but it also supports display templates to show the user what rewards each participant has earned. I was going to add ISP down the road but decided to do it from the start and it ended up being easier than expected. For being my first Alexa app I think the app works extremely well and my kids started to utilize it with no learning curve.

What I learned

Being my first Alexa app I learned a TON. From how to simply use Alexa (still learning tricks) to how to interact with voice commands. I've also never used DynamoDB but the Alexa SDK made that super easy.

What's next for My Rewards

Add support for more languages. My family has been using it for development but exited to see what others think of it.

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Last Updated on May 3, 2021


Cert It! is a web based and android based app that aims to provide and generate certificates over a range of many templates that can be chosen by the user. The user can enter his or her details through a .csv or .xlsx file (containing data in a predefined format having multiple users) or they can list out their own requirements to generate a single certificate.

Problem Statement

There are numerous companies and organizations out there that are providing certificates to their participants / winners. Sometimes , even educational organizations have to provide a load of generated certificates to their people. This process gets pretty hectic since its a very repetitive task.


Cert It! aims to solve this problem. We are providing an all round elucidation into this issue by providing an idea that automates these tasks & at the same time keep it user friendly. Through this application we want to provide our users with

  • Sample templates of our own on which they can choose and select the best possible fit for their organization and participants.
  • Allow the user to upload their own template and generate certificates.
  • Allow the user to upload a snapshot of the handwritten data in a specified format through which our app will recognize the necessary details and map it out to generate a certificate.

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Vehicle Density Based Traffic Optimization/ Management (Ml And Iot)

Last Updated on May 3, 2021



As the vehicle population is rapidly increasing day by day, the cities are facing a huge traffic issue. We know how a traffic system work in current days. Even though there are no vehicles on the green side at the junction, a vehicle on the red side has to wait till the given time. This system has a drawback of traffic delay. On an average most of the junctions in the cities and towns facing this issue


To overcome this problem of over delaying in the traffic, IoT plays a major role. Our idea is: placing IoT sensors at a distance of 100 meters range from the junction we can calculate an average count of vehicles on all the sides. Using this count as main characteristic the traffic light works dynamically that is the roads having high to low count of vehicles has green signal in decreasing order. To increase efficiency of vehicle count we implemented vehicle count using image processing


1. Raspberry Pi

2. Camera (web cam)

3. Spyder.


1. Python


           Vehicle count using image processing



•       At the junction the vehicles coming from different directions may face traffic issues.

•       By image processing technique we can capture the video of number of vehicles coming in different directions.

•       By connecting raspberry pi we can get the count of vehicles in different directions at the junction.

•       Based on the count obtained from different directions the traffic is cleared as per the maximum count of vehicles.

•       The traffic system is prioritized in decreasing order of the count of vehicles ie, the road with high vehicle count will be given first priority and so on.

•       The input in the form of video is captured by web cam, After the vehicle count is done, the control of traffic lights is done using Raspberry Pi

Advantages in real life:

1.Optimizing traffic inconsistency

2.Reducing air pollution

    3.Reducing noise pollution

    4.Priority traffic control


My role in the project:

This is a project for HACKATHON where we team of 8 members worked for 2 days. I worked mostly on the coding part for video processing in the project, I also developed an optimized code for prioritizing the traffic lights, this project contains various code segments one for image processing using cv module in python, one for vehicle count and other for prioritization.

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Telecom Churn Prediction

Last Updated on May 3, 2021


Business Problem Overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Modelling Build models to predict churn. The predictive model that you’re going to build will serve two purposes:

1)It will be used to predict whether a high-value customer will churn or not, in near future (i.e. churn phase). By knowing this, the company can take action steps such as providing special plans, discounts on recharge etc.

2)It will be used to identify important variables that are strong predictors of churn. These variables may also indicate why customers choose to switch to other networks.

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Age And Gender Detection

Last Updated on May 3, 2021


objective :To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture or through webcam.

Description : In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. I used the models trained by Tal hassner and Gil levi. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, I made this a classification problem instead of making it one of regression.

For this python project, I had used the Adience dataset; the dataset is available in the public domain. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.

Working : Open your Command Prompt or Terminal and change directory to the folder where all the files are present.

  • Detecting Gender and Age of face in Image Use Command :

python --image image_name

  • Detecting Gender and Age of face through webcam Use Command :


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