A machine learning model to accurately predict house prices based on various features such as quality, size, and location, utilizing Random Forest and XGBoost algorithms (Python)

Data Cleaning and Data Visualization with python libraries like numpy , pandas, sklean,seaborn, matplotlib-pyplot

Feature Engineering steps implemented in Google Colab with step-by-step view.

Convert a unstructured array into a stuctured dataframe.

Car Sales Price Prediction (Streamlit)

This project will focus on data preparation and will follow the steps : data cleaning, handling text and categorical attributes, and feature scaling.

Mall Customer Segmentation Data

A model that predicts startup success from data on early-stage investments in the Crunchbase database.

Diacritics are short vowels with a constant length that are spoken. The same word in the Arabic language can have different meanings and different pronunciations based on how it is diacritized. In this project, we implement a pipeline to predict the diacritic of each character in an Arabic text using Natural Language Processing techniques.

Analyzing and predicting life expectancy of a country based on multiple factors using multiple regression techniques

Attention Feature Fusion base on spatial-temporal Graph Convolutional Network（AFFGCN）

This project employs a dataset of 103,904 entries with 25 features. Utilizing the XGBoost classifier,The workflow involves data fetching, feature selection, preprocessing, correlation analysis, best feature selection, data rescaling, train-test split, and target balancing. Predicts whether a customer will experience satisfaction with a flight.

The most common discrete and continuous distributions, showing how they find use in decision and estimation problems, and constructs computer algorithms for generating observations from the various distributions. and applications, and lastly the most important concept is covered is entropy

Created numeric features derived from the email text and used those features for logistic regression based on exploratory data analysis. Used logistic regression to train a binary classifier. Used cross-validation to do feature and model selection.

Complete Exploratory Data Analysis on the Housing data of Cook County and fit a linear model to predict house prices

A Machine Learning Approach for House Classification into Expensive and Cheap Categories

Chapter 12: Data Preparation for Fraud Analytics

Semantic Similarity on SNLI dataset using BERT as well as TF-IDF+BERT(Pooled) embeddings.

My very first NLP project where I utilized all the concepts I learned of the topic.