homayounsr Goto Github PK

classification-model-for-speech-emotions

In this project, I used the RAVDESS dataset with eight emotions, each at two intensities. I built a model extracting five features from speech signals to classify emotions, providing valuable insights.

coffee-bean-sales-analysis

In this project, I analyzed Coffee Bean Sales using Python, employing libraries such as pandas, matplotlib

customer_support_system

A machine learning-powered customer support system that automate responses to common queries, prioritize urgent issues, and escalate complex queries to human agents.

flight-delay

In this project, I analyzed flight delays using Python, and I leveraged libraries such as pandas, matplotlib, and seaborn.

gpt-chatbot-for-a-restaurant-with-flask-and-rag

In this project, I built a chatbot to answer customer simple inquiries about restaurants, such as displaying the food menu and contact, and providing information about the number of available tables for reservation.

homayounsr

homayounsr.github.io

Personal Portfolio

housing-price-prediction-using-linear-regression

In this project I built a linear regression algorithm from scratch to predict the price of the houses based on different features

imdb-web-scraping-using-python

python-review-scraping-imdb-movie-site-with-BeautifulSoup

interactive_dashboard_using_streamlit_and_python

In this project, I have built an interactive dashboard for a supermarket dataset using streamlit framework

predict-housing-price-using-linear-regression-and-sklearn

Predict house price with linear regression

prediction-model-for-nyc-housing-listings

The objective of this project was to predicting housing prices for New York City. To achieve this goal, I performed web scraping to collect a dataset of 17,000 records from realtor.com, covering all cities in the state of New York. Employing transfer learning, I utilized a model trained on 10,000 records to further train a model using 2,000 records

sentiment-classification-using-imdb-reviews

For this project, I aimed to perform sentiment analysis on IMDB movie reviews. My dataset consisted of over 36,000 reviews, each accompanied by movie ratings ranging from 0 to 10. The primary objective was to construct a machine learning model capable of categorizing reviews into three sentiment classes: negative, neutral, and positive.