Anas Zafar's Projects
This project implements an automated bot (Autobot) using Python and Selenium to scrape product data from Amazon. It extracts details such as product title, price, image URL, and reviews, storing the data in JSON files. Additionally, it provides a FastAPI-based web API for accessing the scraped product data.
This project is aimed at automating the process of sending messages on Facebook using the Python Selenium library. It can be used to send messages to friends or anyone on Facebook.
This project aims to automate the process of messaging on Whatsapp. Using Selenium in Python, the script will open Whatsapp Web, search for the desired contact and send the pre-defined message.
This project uses selenium to automatically search for videos on YouTube. It is implemented from scratch in python and is executed in a jupyter notebook (.ipynb) file.
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.
Classify individuals using Census Income dataset and Naïve Bayes/Logistic Regression. Preprocess data, train algorithms, evaluate performance with accuracy, precision, recall, & F1 score.
A project that plays an animation video and pauses the video where the center of a green circle and a blue circle exactly overlap (or very close to each other).
This project aims to conduct sentiment analysis on product reviews collected from Daraz website. The user can choose the desired product category to scrape data from. The collected data will be cleaned and preprocessed, and then the sentiment of the reviews will be analyzed
Distributed System Data Base implemented in C++ using B-Trees to manage and manipulate the death rate dataset efficiently.
In this project, we will perform Exploratory Data Analysis (EDA) on three datasets, `ufo`, `u.user` and `movies`. We will use the Python library Pandas for data cleaning, transforming, and manipulation and Matplotlib for data visualization.
This project is focused on performing Exploratory Data Analysis (EDA) on a dataset using Pandas. The goal of EDA is to get insights about the data, identify patterns and relationships, and prepare the data for further analysis or modeling.
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.
This repository contains an implementation of finding the wave with the highest number of frequencies (peaks) from an image, and then drawing a bounding rectangle around it.
The Game of Life is a simple yet powerful simulation that demonstrates how complex behavior can emerge from a small set of simple rules.
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.
This project aims to analyze the Grammy award data to understand the trends and patterns in the award distribution among young and old artists. The objective is to determine if the claim that judges are inclined to give the Grammy to old artists is true or false.
This repository contains a script that utilizes OpenCV to find the boundary of a hand in an image and display it on the original image.
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.
A website frontend created using HTML, CSS, and JavaScript for hotel management purposes.
Predicting house prices using Linear Regression and GBR
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.
This repository contains a solution to find the corners of an object in an image using the Sobel Edge Detector and Thresholding, and then use these corners to calculate the area of the object.
This repository contains an implementation of finding the overlapping area between two objects using OpenCV.
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
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.
This project aims to compare the adoption of the internet in Denmark and Belarus and determine if income level has an impact on the speed of adoption. The data used for this analysis is from the World Bank Data (1990-present) and is stored in the file "WorldBankData.csv".
A Python program to scrape Latin words and related information from the Dickinson College Commentaries website using BeautifulSoup, Requests, and other tools.
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.
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.
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.