Air Quality Analysis And Prediction Of Italian City

Last Updated on May 3, 2021


Problem statement

  • Predict
  • The value of CO in mg/m^3 reference value with respect to the available data. Please assume if you need, but do specify the same.
  • Forecast
  • The value pf CO in mg/m^3 for the next 3 3 weeks on hourly averaged concentration

Data Set Information

located on the field in a significantly polluted area, at road level,within an Italian city. Data were recorded from March 2004 to February 2005 (one year)representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer. Evidences of cross-sensitivities as well as both concept and sensor drifts are present as described in De Vito et al., Sens. And Act. B, Vol. 129,2,2008 (citation required) eventually affecting sensors concentration

Data collection

0 Date (DD/MM/YYYY)

1 Time (HH.MM.SS)

2 True hourly averaged concentration CO in mg/m^3 (reference analyzer)

3 PT08.S1 (tin oxide) hourly averaged sensor response (nominally CO targeted)

4 True hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer)

5 True hourly averaged Benzene concentration in microg/m^3 (reference analyzer)

6 PT08.S2 (titania) hourly averaged sensor response (nominally NMHC targeted)

7 True hourly averaged NOx concentration in ppb (reference analyzer)

8 PT08.S3 (tungsten oxide) hourly averaged sensor response (nominally NOx targeted)

9 True hourly averaged NO2 concentration in microg/m^3 (reference analyzer)

10 PT08.S4 (tungsten oxide) hourly averaged sensor response (nominally NO2 targeted)

11 PT08.S5 (indium oxide) hourly averaged sensor response (nominally O3 targeted)

12 Temperature in °C

13 Relative Humidity (%)

14 AH Absolute Humidity.

More Details: Air quality analysis and Prediction of Italian city

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Last Updated on May 3, 2021


Problem Statement

Develop tools that would increase Productivity for students and teachers. In the past 10-15 years we have seen the transition of things around us from offline to online, whether it's business, entertainment activities, daily needs, and now even education. Productivity tools have been a success with businesses and firms. Develop productivity tools for students and teachers in any domain of your choice that can achieve the same success in the educational field in the future.

Problem Solution

In this post - covid era, the education sector has erupted, with a plethora of new opportunities. Scholastic provides a complete and comprehensive education portal for students as well as staff.

  • The USP of the application are lab sessions simulated using Augmented Reality.
  • Other features include usage of virtual assistants like Alexa to provide reminders, complete timetable and file integration
  • A blockchain based digital report card system where teachers can upload report cards for students & send it to parents.
  • Plagiarism checker for assignments. It is a one - stop solution to all needs such as announcements and circulars from institution or a staff member, fee payment and even a chatbot for additional support.

Tech Stack

  • Google Assistant For Chatbot
  • Via the Actions Console
  • Python3 for Plagiarism Checker
  • Gensim
  • NumPy
  • NLP Models ( Word Embedding)
  • Heroku (For Deployment & making API Calls)
  • Android Studio with Java For Main Android App
  • AR Foundation For Simulated Lab Sessions with Blender & Unity
  • Ethereum, Solidity & React.js For Blockchain Based Storage for Report Cards (Along with Ganache & Truffle Suite)

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Credit Card Detection

Last Updated on May 3, 2021


models trained to label anonymized credit card transactions as fraudulent or genuine. Dataset from Kaggle. In this project I build machine learning models to identify fraud in credit card transactions. I also make several data visualizations to reveal patterns and structure in the data.

The dataset, hosted on Kaggle, includes credit card transactions made by cardholders. The data contains 7983 transactions that occurred over of which 17 (0.21%) are fraudulent. Each transaction has 30 features, all of which are numerical. The features V1, V2, ..., V28 are the result of a PCA transformation. To protect confidentiality, background information on these features is not available. The Time feature contains the time elapsed since the first transaction, and the Amount feature contains the transaction amount. The response variable, Class, is 1 in the case of fraud, and 0 otherwise. Project Introduction

The approaches for the project are :

Randomly split the dataset into train, validation, and test set.
Do  feature engineering.
Predict and evaluate with validation set.
Train on  train set then predict and evaluate with validation set.
Try other different models.
Compare the difference between the predictions and choose the best model.
Predict on test set to report final result.

Data Description

I was able to accurately identify fraudulent transactions using a LogisticRegression model. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. Feature 'Class' is the target variable with value 1 in case of fraud and 0 otherwise.To improve a particular model, I optimized hyperparameters via a grid search with 3-fold cross-validation


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Salary Predictor

Last Updated on May 3, 2021


This is a web app created using open source python library called Streamlit. This library is mainly used to create web apps for machine learning and data science. In this Project I collected data required from the

Kaggle. I Used Sklearn library to get the model required for the data and I fitted the data using in-built methods in it. So I created a web app which contain two pages named Home and Prediction. In home page I displayed the data collected and a scatter graph plotted using the matplotlib library with the help of data collected from Kaggle. In prediction page there will be a text filed where we can enter the experience of the employee and click the button which ultimately shows the precited salary for that employee. Stream lit Web app gives the output of a local host URL. So we have to deploy it globally. So I deployed the web app in Heroku platform. Here in this project I just downloaded a small data set to test how it works. So here a large data set can also be taken but the process will be different in training the model. For large datasets the data should be split to train and testing data so that we can train the model accurately and advanced algorithms to train the model is also used. So based on our convenience and requirements we can do machine learning models and save it into a file and this file can be used while creating a web app.

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Task-Manager Backend Rest-Api(Node.Js)

Last Updated on May 3, 2021


Technology Used:

  • Node.js
  • Express js
  • MongoDB

Library Used:

  • jwt (json web token)
  • bcrypt
  • validator
  • sharp
  • multer

General Description:

  • In this project user can create its own tasks.
  • User can manage their tasks according to their preferences.
  • User can edit or delete the particular task and also user can track the status of task (i.e completed or pending).


  • In order to use application you should register in an application. You can make it by calling Sign Up API.
  • The password is stored in Encrypted format in database.
  • In Login API we generate an access token using jwt.
  • In order to call create,update,delete API'S we have to pass an access token in header section of the request.
  • If we don't pass an access token then user will got a message 'Please Authenticate'.

Database Structure:


description : String,

completed :Boolean,

owner : ObjectId,

timestamps :true


name : String,

email :String,

password :String

age : Number,




avtar : Buffer



URL TYPE Description

  • /users/login POST login
  • /users/ POST SignUp
  • /users/me GET Profile
  • /users/logout POST logout
  • /users/logoutall POST logout from all devices
  • /users/me DELETE delete user
  • /users/me PUT Updating user
  • /upload POST Uploading avtar
  • /users/me/avtar DELETE delete user avtar


URL TYPE Description

  • /task POST Create Task
  • /task GET Getting Task
  • /task/:id PUT Updating Task
  • /task/:id DELETE Deleting Task
  • /users/logoutall POST logout from all devices
  • /users/me DELETE delete user

More Details: Task-Manager Backend REST-API(Node.js)

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Chess Game

Last Updated on May 3, 2021


Implementation of chess in python language in which 2 players can play this game. Using "R" key we can undo this game and "Q" the game ends in between. There is 2 Classes in code.

  1. One is GameState() which will store the positions of the pieces.
  2. It has changePosition() function which will change the position of the pieces as per request.
  3. undoSteps() will undo to the previous state.
  4. availablePositions() will give all the possible moves a piece can move at that position. availablePositions() will call respective functions of pieces like bishopAvailablePositions(), queenAvailablePositions(), etc. Pawn can move in forward direction and in left or right only if opposite color piece is there. For rook, we create directions=((-1,0),(0,-1),(1,0),(0,1)) where each set() represents the direction that piece can move. Similar we use different logic for other pieces as well.
  5. Another Class is Steps() which is used as a data structure to store initial and final positions of rows and columns as well as unique number(unique ID), attacking piece and piece captured.

To get input from user, pygame is used. Whenever user clicks on game window, we get the position, where we clicked, using mouse.get_pos(). Some graphics is also used to make the game more enjoyable.

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