Muhammad Hassaan Farooq Butt's repositories
Antenna-design-using-ML
In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
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
Github home page customization.
Mailing_Merge_Using_Google_API
In this repo, I merge google sheet with google Docs using google API for automation purposes.
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.
Association-Rule-Mining-using-Apriori-and-FP-Growth
Implementation association rule mining using APRIORI and FP GROWTH using python
FCHCNN-for-HSIC
This repo is implementation of research article "A Fast and Compact Hybrid CNN for Hyperspectral Imaging-based Bloodstain Classification".
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.
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.github.io
A community maintained open source project aimed at making a personal portfolio for researchers, developers, and analysts simple, fast, and less cumbersome.
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.
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.
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.
Employee_Data_Management_Project_Using_R
In this project we performed PCA, LDA and Ridge Regression
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.
Grey_Scale_Masks_to_RGB_Conversion
Converting Greyscale Masks to RGB images by assigning each class of mask a color using OpenCV
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.
Impact-of-Covid-on-IT-companies-stock-exchange-shares
We try to analysis the impact of covid 19 on Stock Exchange ( IT companies)
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.
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.
Python_Fun_Scripts
Just For Fun
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.
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.
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.