karimosman89

ab-testing

Analyzes the results of A/B tests to determine if there is a statistically significant difference between control and treatment groups. It provides a structured approach for performing A/B tests, interpreting results, and making data-informed decisions. A valuable resource for marketers and product managers aiming to optimize user experience.

ADAH_Project

A collection of Jupyter Notebooks covering data wrangling, visualization, and machine learning algorithms.

AI-Project

This project, you will build a full AI pipeline for an image classification task using Convolutional Neural Networks (CNNs). The project will cover data ingestion, preprocessing, model training, deployment, and CI/CD integration using GitHub Actions, Docker, and AWS.

AIRFLOW

API

automl-framework

Develop a framework that automatically selects the best machine learning model and hyperparameters for a given dataset. Implement techniques like Bayesian optimization for hyperparameter tuning.

Data-Pipeline

This project implements a scalable ETL pipeline that processes streaming data from Kafka and performs analytics using Spark.

reinforcement-learning

Create an agent that learns to play a game (e.g., Atari, chess) using reinforcement learning algorithms like Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO).

Time-Series-Forecasting

A collection of Jupyter Notebooks covering data wrangling, visualization, and machine learning algorithms.

X_Ray_Project

A collection of Jupyter Notebooks covering data wrangling, visualization, and machine learning algorithms.

karimosman89/traffic-flow-prediction

Develop a deep learning model capable of predicting traffic flow in urban environments. The model will utilize historical traffic data, weather conditions, and road configurations to forecast traffic patterns. This information can be invaluable for traffic management systems, helping to optimize traffic signals and reduce congestion, ultimately.

karimosman89/predictive-maintenance

a predictive maintenance system using IoT sensor data to predict equipment failures before they occur. Use time series analysis and machine learning for predictions.

karimosman89/ab-testing

Analyzes the results of A/B tests to determine if there is a statistically significant difference between control and treatment groups. It provides a structured approach for performing A/B tests, interpreting results, and making data-informed decisions. A valuable resource for marketers and product managers aiming to optimize user experience.

karimosman89/API

karimosman89/automl-framework

Develop a framework that automatically selects the best machine learning model and hyperparameters for a given dataset. Implement techniques like Bayesian optimization for hyperparameter tuning.

karimosman89/Chatbot

Develop a sophisticated chatbot that can maintain context in conversations using a memory mechanism. Implement user intent recognition and response generation using advanced NLP models.

karimosman89/data

A collection of Jupyter Notebooks covering data wrangling, visualization, and machine learning algorithms.

karimosman89/Data-Pipeline

This project implements a scalable ETL pipeline that processes streaming data from Kafka and performs analytics using Spark.

karimosman89/data-warehousing

This project focuses on creating a simple data warehouse using SQLite. It includes scripts for table creation, data loading, and querying the database, providing a foundational understanding of data warehousing concepts. This project is ideal for those interested in database management and data retrieval.

karimosman89/DeepLearning_ImageClassification_Binary

karimosman89/DevOps-Project

The DevOps Project is a full-stack web application that illustrates essential DevOps practices, including Continuous Integration (CI), Continuous Deployment (CD), Infrastructure as Code (IaC), and container orchestration.

karimosman89/etl-pipeline

Implements an ETL (Extract, Transform, Load) pipeline to process raw data and create a clean, transformed dataset. It demonstrates best practices in data engineering and includes scripts for extracting data from various sources, transforming it into a usable format, and loading it into a database. Perfect for aspiring data engineers ETL skills.

karimosman89/FastApi

karimosman89/GitHubIssueClassification

karimosman89/graph-nn

Analyze social networks using Graph Neural Networks (GNNs) to predict user behavior, detect communities, or classify nodes. Create visualizations to represent the social network and results.

karimosman89/image-recognition

Demonstrates how to build an image recognition system using a Convolutional Neural Network (CNN). It encompasses the complete pipeline from data preprocessing to model training and prediction. This project serves as an excellent introduction to deep learning and computer vision, providing practical insights into image classification tasks.

karimosman89/IrisFlowerClustering

karimosman89/ML-Pipeline-AWS

This project aims to build a machine learning pipeline that predicts customer churn using AWS services like SageMaker for model training and deployment, along with Docker for containerization.

karimosman89/ML_Model_Project

karimosman89/object-detection

Create a real-time object detection system using the YOLO (You Only Look Once) model. Implement a video stream processing application that can detect and classify objects in real-time.

karimosman89/predictive-modeling

This project focuses on developing a predictive model using machine learning techniques to analyze a given dataset. The model is designed to provide accurate predictions based on historical data. It includes scripts for data preparation, model training, and making predictions, along with structured approach to project organization and documentation

karimosman89/recommendation-system

A recommendation system that combines collaborative filtering and content-based filtering techniques to provide personalized recommendations (e.g., movies, products). Implement it as a web service.

karimosman89/reinforcement-learning

Create an agent that learns to play a game (e.g., Atari, chess) using reinforcement learning algorithms like Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO).

karimosman89/RestaurantViolations

karimosman89/RestaurantViolationsPrediction_Api

karimosman89/RestaurantViolationsPrediction_Console

karimosman89/sentiment-analysis

Build a sentiment analysis tool that processes user reviews from various platforms (like Amazon or Yelp) and provides insights on sentiment trends over time. Use advanced NLP techniques like Transformers (BERT, GPT).

karimosman89/X_Ray_Project

A collection of Jupyter Notebooks covering data wrangling, visualization, and machine learning algorithms.

karimosman89/karimosman89

karimosman89/prediction-powerbi

This project focuses on building a customer churn prediction model using machine learning and visualizing the results with Power BI. You will use Python for churn prediction and Power BI for generating interactive reports that display key metrics such as churn rate, customer demographics, and predictive insights.