Anas Zafar's repositories
Intensity-Slicing-and-Object-Count
This project implements intensity slicing to separate objects in a digital image and counts the number of pixels for each object. The implementation uses OpenCV library for image processing tasks.
Image-Processing-Tallest-Finger-Detection
Find the tallest finger in your hand images with ease! This repo provides a solution using computer vision & image processing. Written in Python with OpenCV & Numpy. Simply run the code from the command line with the input image path. Enhance your app's capabilities today
Image-Processing-Brightness-and-Contrast-Transformations
This repository contains implementations of different brightness and contrast transformations that can be applied to images, including Log and Inverse Log transform, Power law nth power and nth root, and Power Law transformation.
ExploratoryDataAnalysis_On_NUCES_Admission_Dataset
This project preprocesses and analyzes NUCES University's admission dataset. The data will be cleaned and transformed to reveal valuable insights and improve the admission process efficiency. The project uses Pandas, Matplotlib, and Seaborn. The results will provide valuable insights into admission criteria and distribution.
Line_And_Scatter_Plot_Using_D3_On_Movies_Dataset
This project demonstrates the use of D3.js to plot a scatter plot (budget vs revenue) and line plot (average revenue over time) using a movies dataset.
London_GeoJson_MapVisualization_using_D3
Discover London GeoJson data with our interactive Graph & Map Visualization Solution using D3. View data with map, graph, & timeline visualizations. Advanced interactivity: pan, zoom, select, brush, link.
Metro_Data_WareHouse
The METRO DW prototype uses Mesh Join & Star Schema for sales, customer & inventory data analysis. Implemented in SQL & Java for fast, accurate, & consistent data retrieval. Offers valuable insights & can be queried with standard BI tools.
Multimodal_Memes_Classification
Build a PyTorch-based multimodal architecture to classify memes using image & caption. Trained on a meme classification dataset, MLP architecture uses PyTorch, Numpy, Matplotlib, & Sklearn to achieve improved performance compared to baselines.
Particle-_Swarm_Optimization-PSO-_for_Feature_Selection
PSO feature selection improves classifier performance. Implemented in Jupyter Notebook with pandas, numpy, scikit-learn. PSO done from scratch. Results compared using accuracy, precision, recall, F1 score. Improves results compared to using all features. Can be applied to various classification problems.
Genetic_Algorithm_for_Feature_Selection
Implements a genetic algorithm to select the most impactful features in a dataset to improve classifier performance. Written in Jupyter Notebook using pandas, numpy, scikit-learn. Results displayed with accuracy, precision, recall, F1 score comparison to using all features.
Census_Income_DataSet_Classification
Classify individuals using Census Income dataset and Naïve Bayes/Logistic Regression. Preprocess data, train algorithms, evaluate performance with accuracy, precision, recall, & F1 score.
Segmentation_And_Classification_of_Diabetic-Retinopathy
Develop machine learning model to detect diabetic retinopathy in eye images using k-mean clustering, image segmentation, and image classification with ANN, CNN, ML. Requires Numpy, OpenCV, Tensorflow, Sklearn.
Object_Recognition_In_CIFAR-10_Images_Dataset
This project performs object recognition using CIFAR-10 and CNN/Random Forest models in Python. It preprocesses data, trains models, evaluates performance and compares results with the goal of high accuracy and feature importance understanding. The final output includes a comparison of the models and insights.
Heart_Attack_Prediction
Classify Heart Attack dataset using 3+ ML models and perform Exploratory Data Analysis for insights. Preprocess data, apply majority voting for final prediction, aim for accuracy & F-score over 65%. Use Numpy, Pandas, Sklearn, Matplotlib. Final report & insights on methodology & results expected. Run code in Jupyter Notebook.
Distributed_System_DataBase
Distributed System Data Base implemented in C++ using B-Trees to manage and manipulate the death rate dataset efficiently.
CandyCrush_Game
A C++ implementation of the popular Candy Crush game using object-oriented programming & SFML library for graphics. Matches candies to score, swap adjacent candies. Game ends when score reaches a certain level or out of moves. User input via mouse clicks. No saved progress.
Puzzle_Game
A C++ command line puzzle game where player rearranges pieces to complete a pattern. Input: puzzle size, piece state. Output: puzzle state after moves. ASCII characters represent pieces, no progress saved.
Maze_Game
A C++ implementation of a command line maze game where the player navigates through a maze, avoiding walls and obstacles to reach the exit. Input includes maze size and layout, and output shows the maze and player's position after each move. ASCII characters represent the maze and player.
Game_of_Life
The Game of Life is a simple yet powerful simulation that demonstrates how complex behavior can emerge from a small set of simple rules.