Pinned Repositories
aim-trainer
ASLDetection
Automated-EQ
BE Project Code dump
AutoML
AutoML is a distributed machine learning pipeline designed to scale to large datasets. AutoML aims to automate the entire process of solving a classification problem. It just requires the dataset and the target column as an input and then the system takes care of the rest. Efficient cleaning of the dataset is performed, which imputes all the missing values and gives better structure to the dataset. The system is capable of detecting categorical values, thus performing One-Hot Encoding where required. Further, in the preprocessing stage, it also takes care of feature engineering, dimensionality reduction, sampling and removal of outliers which affect the accuracy of the model. After the preprocessing stage, the ready data is trained on several models, with multiple different hyperparameters. The output of the system is the name, accuracy and code of the best model, which is judged based on its accuracy. The system is tested on over 30 datasets, both binary and multi-class classification and there is a robust system to quickly train any dataset given to it.
machine-translation
MED-277_Chatbot
microexpressions_meview
re-bag
👜 A real-time auction site for reselling antiques and luxury products. Sem IV OSTL project.
tahlia-stanton-website
Time-Series-Analysis-of-Air-Quality-Data
This work aims to analyze the air quality in India and the effects of seasons and COVID-19 on the concentration of pollutants in the air and thereby their effect on the air quality index (AQI). The analysis is performed on a full scale, taking into consideration different levels of granularities such as daily, weekly and monthly data. This study performs extensive preprocessing of the time series data for air quality to make it output the best results. The results evidenced that particulate matter i.e., PM 2.5 and PM 10 have the greatest impact on air quality. Analysis of the effect of change in seasons on the overall air quality has been carried out, along with the impact of the nationwide lockdown due to COVID-19, which led to a substantial improvement in the AQI levels. Furthermore, we also use the state-of-the-art forecasting algorithm Prophet to predict the monthly average air quality index and compare it with the actual recorded values, giving us a highly accurate prediction. We also performed a comparative analysis of AQI for the cities of Delhi and Bengaluru, having different seasons and climates, which results in valuable insights on to what extent the environmental factors affect the air quality measures of that location.
Samitkk18's Repositories
Samitkk18/Time-Series-Analysis-of-Air-Quality-Data
This work aims to analyze the air quality in India and the effects of seasons and COVID-19 on the concentration of pollutants in the air and thereby their effect on the air quality index (AQI). The analysis is performed on a full scale, taking into consideration different levels of granularities such as daily, weekly and monthly data. This study performs extensive preprocessing of the time series data for air quality to make it output the best results. The results evidenced that particulate matter i.e., PM 2.5 and PM 10 have the greatest impact on air quality. Analysis of the effect of change in seasons on the overall air quality has been carried out, along with the impact of the nationwide lockdown due to COVID-19, which led to a substantial improvement in the AQI levels. Furthermore, we also use the state-of-the-art forecasting algorithm Prophet to predict the monthly average air quality index and compare it with the actual recorded values, giving us a highly accurate prediction. We also performed a comparative analysis of AQI for the cities of Delhi and Bengaluru, having different seasons and climates, which results in valuable insights on to what extent the environmental factors affect the air quality measures of that location.
Samitkk18/MED-277_Chatbot
Samitkk18/aim-trainer
Samitkk18/ASLDetection
Samitkk18/Automated-EQ
BE Project Code dump
Samitkk18/AutoML
AutoML is a distributed machine learning pipeline designed to scale to large datasets. AutoML aims to automate the entire process of solving a classification problem. It just requires the dataset and the target column as an input and then the system takes care of the rest. Efficient cleaning of the dataset is performed, which imputes all the missing values and gives better structure to the dataset. The system is capable of detecting categorical values, thus performing One-Hot Encoding where required. Further, in the preprocessing stage, it also takes care of feature engineering, dimensionality reduction, sampling and removal of outliers which affect the accuracy of the model. After the preprocessing stage, the ready data is trained on several models, with multiple different hyperparameters. The output of the system is the name, accuracy and code of the best model, which is judged based on its accuracy. The system is tested on over 30 datasets, both binary and multi-class classification and there is a robust system to quickly train any dataset given to it.
Samitkk18/cowin-alert
Samitkk18/CSES
Samitkk18/legal-NER
Samitkk18/machine-translation
Samitkk18/microexpressions_meview
Samitkk18/re-bag
👜 A real-time auction site for reselling antiques and luxury products. Sem IV OSTL project.
Samitkk18/tahlia-stanton-website
Samitkk18/CSE291_AWS_Analysis
Samitkk18/deeplearning.ai-deeplearning_specialization
Samitkk18/doctor-clinic-app
Samitkk18/DSC_208R_PA3_WI24
Samitkk18/First-Repo
Samitkk18/GitHubGraduation-2022
Join the GitHub Graduation Yearbook and "walk the stage" on June 11.
Samitkk18/googlemapsapi
Samitkk18/launch-jobs
🚀💼
Samitkk18/Machine-Learning-AndrewNg-exercise-solutions
Samitkk18/Machine-Learning-with-Python
Python code for common Machine Learning Algorithms
Samitkk18/nlp
Re-learning the basics of NLP with some implementation
Samitkk18/OTP
Samitkk18/samitkk18
Samitkk18/Samitkk18.github.io
https://samitkk18.github.io
Samitkk18/School-Management-System-DBMS
Samitkk18/StrokePrediction
Samitkk18/Time-Series-Prediction