Pinned Repositories
Antenna-design-using-ML
In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
Association-Rule-Mining-using-Apriori-and-FP-Growth
Implementation association rule mining using APRIORI and FP GROWTH using python
Covid_Prediction_based_on_medical_symptoms
In this project I considered several medical symptoms to predict using logistic regression either the person is infected or not and also provide some recommendations to him/her.
Crime-Spark-ML
In this project I stream data and do crime classification using Spark. This dataset contains incidents derived from the SFPD Crime Incident Reporting system. The data ranges from 1/1/2003 to 5/13/2015. I do some data analysis of crime scenes in different areas and with respect to other parameters.
Discrete-event-simulation
I designed a network packet routing system. To do so, I define the system and run a simulation so that I can generate data that will be used for the optimization of design parameters.
Flight-Delays-Prediction
In this project, I used Decision Tree Learning Model as the main algorithm to build the model. Due to the big amount of flight data, we implement the project using MRJob, PySpark and Spark's MLlib then compare the performance and accuracy of those implementations.
Mailing_Merge_Using_Google_API
In this repo, I merge google sheet with google Docs using google API for automation purposes.
MathChallenge
Have the function MathChallenge(str) take the str parameter being passed and evaluate the mathematical expression within in. For example, if str were "2+(3-1)*3" the output should be 8. Another example: if str were "(2-0)(6/2)" the output should be 6. There can be parenthesis within the string so you must evaluate it properly according to the rules of arithmetic. The string will contain the operators: +, -, /, *, (, and ). If you have a string like this: #/#*# or #+#(#)/#, then evaluate from left to right. So divide then multiply, and for the second one multiply, divide, then add. The evaluations will be such that there will not be any decimal operations, so you do not need to account for rounding and whatnot.
MHassaanButt
Github home page customization.
Rice-Disease-Classfication
In this project, I used Hybrid deep CNN transfer learning on rice plant images, perform classification and identification of various rice diseases. I employed Transfer Learning to generate our deep learning model using Rice Leaf Dataset from a secondary source. The proposed model is 90.8% accurate, Experiments show that the proposed approach is viable, and it can be used to detect plant diseases efficiently.
MHassaanButt's Repositories
MHassaanButt/Antenna-design-using-ML
In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
MHassaanButt/Rice-Disease-Classfication
In this project, I used Hybrid deep CNN transfer learning on rice plant images, perform classification and identification of various rice diseases. I employed Transfer Learning to generate our deep learning model using Rice Leaf Dataset from a secondary source. The proposed model is 90.8% accurate, Experiments show that the proposed approach is viable, and it can be used to detect plant diseases efficiently.
MHassaanButt/MHassaanButt
Github home page customization.
MHassaanButt/Covid_Prediction_based_on_medical_symptoms
In this project I considered several medical symptoms to predict using logistic regression either the person is infected or not and also provide some recommendations to him/her.
MHassaanButt/Association-Rule-Mining-using-Apriori-and-FP-Growth
Implementation association rule mining using APRIORI and FP GROWTH using python
MHassaanButt/FCHCNN-for-HSIC
This repo is implementation of research article "A Fast and Compact Hybrid CNN for Hyperspectral Imaging-based Bloodstain Classification".
MHassaanButt/The_Schelling_Model
The Schelling model of segregation is an agent-based model that elucidates how individual preferences for neighbours might result in segregation. When agents represent householders who relocate to the city, the model is particularly efficient for studying ethnic group residential segregation.
MHassaanButt/Brain_Tumor_Classfication_Using_DL
MHassaanButt/Decision_Tree_On_Multiple_Datasets
The goal of this project is to implement the supervised strategy of the class decision Tree on different datasets.
MHassaanButt/mhassaanbutt.github.io
A community maintained open source project aimed at making a personal portfolio for researchers, developers, and analysts simple, fast, and less cumbersome.
MHassaanButt/Comparision-of-ML-and-DL-models-for-Cifar-10
I do comparison of KNN, SVM (polynomial and Gaussian Kernel) and CNN (Sequential and RESNET) on Cifar-10 dataset.
MHassaanButt/Cricket_Predictions
In this project, I predict a winner for the ashes ODI series between England and Australia where the away team is England and the home team is Australia using a machine learning algorithm.
MHassaanButt/EDA_and_k-means_on_FreshCo_Data
Perform an exploratory data analysis on the data and then use k-means to produce a cluster analysis.
MHassaanButt/Employee_Data_Management_Project_Using_R
In this project we performed PCA, LDA and Ridge Regression
MHassaanButt/Face_Recongnition_using_Deep_Learning_Techniques
MHassaanButt/finding_prodcutivity_of_diff_departements_using_regression_models
In this case study, we have dataset of a company for case study to find actualy proudcity of its workers using regression models. I draw a comparative analysis of different machine learning algorithms on the basis of mean absolute error.
MHassaanButt/Grey_Scale_Masks_to_RGB_Conversion
Converting Greyscale Masks to RGB images by assigning each class of mask a color using OpenCV
MHassaanButt/Human_Activity_Recognition_Using_ML-NN
Demonstrate Machine Learning Algorithm and Neural Networks for Human activity recognition using smartphone data. The dataset is from the UCI Machine Learning Repository.
MHassaanButt/Impact-of-Covid-on-IT-companies-stock-exchange-shares
We try to analysis the impact of covid 19 on Stock Exchange ( IT companies)
MHassaanButt/Implementing_Different_Trees_Models_on_US_Companies_Data
In this project I implemented decision tree, bagged tree, random forest and XGBoost for comparison of better MAE performance between Trees Algorithms.
MHassaanButt/Netflix-Analysis-using-R
MHassaanButt/newspaper-scraping
Atomically Newspaper Scrapping Using Beautiful Soup. Only three Categories of news are scraped including national, international and latest. News Summarization, Text Classification, Sentimental Analysis, WordCloud and many more NLP stuff is included.
MHassaanButt/Python_Fun_Scripts
Just For Fun
MHassaanButt/real-time-emotion-recognition
A web application for real-time emotion recognition using Vision Transformer (ViT). The app processes video input to generate an output video with an accompanying graph displaying emotion values for each frame.
MHassaanButt/trends-awarness-backend
I have built a backend API in Flask that scrapes data from Twitter based on hashtags and keywords and returns a JSON response. To do this, I installed the necessary Python packages, authenticated with Twitter API, created a Flask app, defined an endpoint for scraping data, parsed the data and returned a JSON response.
MHassaanButt/URDU_Sentiment_Analysis
MHassaanButt/adversial_attacks_and_defense_on_evs
MHassaanButt/Attendence_Management_System_through_Face_Recognition
This project is for automatical attendence system where user can capture images of particular person on runtime and train it. Also track that person and maintain record.
MHassaanButt/Data_Wrangling_Using_R
MHassaanButt/visionwise
A web application leveraging the Florence 2 model for advanced image captioning and visual question answering. Explore the intersection of AI and computer vision with a user-friendly interface. Contributions are welcome!