Muhammad Hassaan Farooq Butt's Projects
In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
Implementation association rule mining using APRIORI and FP GROWTH using python
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.
In this project I used past three years data of climate having key attributes like rainfall, wind speed, direction, temperature etc and predict calamity on the basis of these key attributes for next year three years using logestic regression technique and also perform descriptive data analysis.
I do comparison of KNN, SVM (polynomial and Gaussian Kernel) and CNN (Sequential and RESNET) on Cifar-10 dataset.
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.
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.
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.
The aim of this repository to guide the new students who wants to learn how basic algorithms of data mining (Apriori, Kmeans and KNN etc) are implemented
The goal of this project is to implement the supervised strategy of the class decision Tree on different datasets.
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.
Perform an exploratory data analysis on the data and then use k-means to produce a cluster analysis.
In this project we performed PCA, LDA and Ridge Regression
This repo is implementation of research article "A Fast and Compact Hybrid CNN for Hyperspectral Imaging-based Bloodstain Classification".
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.
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.
In this project, I am applying your frequentist inference and regression modelling skills to different datasets. I applied several machine learning algorithms and try to answer research questions of related problems and also perform data visualization to justify my results.
Converting Greyscale Masks to RGB images by assigning each class of mask a color using OpenCV
Demonstrate Machine Learning Algorithm and Neural Networks for Human activity recognition using smartphone data. The dataset is from the UCI Machine Learning Repository.
We try to analysis the impact of covid 19 on Stock Exchange ( IT companies)
In this project I implemented decision tree, bagged tree, random forest and XGBoost for comparison of better MAE performance between Trees Algorithms.
In this repo, I merge google sheet with google Docs using google API for automation purposes.
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.
Github home page customization.
A community maintained open source project aimed at making a personal portfolio for researchers, developers, and analysts simple, fast, and less cumbersome.