Ames Housing Price Predictor is an ML project focused on predicting housing prices in Ames, Iowa. Development spans all stages of the machine learning model lifecycle, from data exploration and cleansing, through feature engineering and model selection, to deploying a working API using FastAPI, Docker, and GCP.

Project using multiple linear regression to model prices of houses in Ames, IA.

my attempt to train a model on Ames housing dataset with scikit-learn and XGBoost .

MSDS 410 Data Modeling for Supervised Learning (R)

This is a house price prediction study which utilized Exploratory Data Analysis, Dealing with Missing Values, Linear Regression with LASSO and Ridge regularization to predict house prices in the Ames Housing Data Set

Prediction of house sale prices based on the Ames Housing dataset

🏡 Built linear regression model to predict house prices in Ames dataset with applied tools such as scikit-learn pipeline

I performed data cleaning on the Ames housing dataset. This was to showcase my data cleaning skills and data cleaning process.

This repository contains my assignments and projects related to deep learning, including implementations of fundamental concepts such as Linear Regression, Gradient Descent, Multi-Layer Perceptron (MLP), and more. Each section includes code, explanations, and relevant documentation. The goal of this repository is to showcase my learning journey.

🧠 Foundational Deep Learning Projects built from scratch using Python & NumPy. Includes Linear & Logistic Regression, Neural Networks, and MLP classification with visualizations. Ideal for beginners and ML enthusiasts!

A collection of small data science projects to predict house pricing for two different datasets

Predicting sales prices for Ames housing dataset

Predicting house prices using advanced regression techniques

Ames dataset: House Price prediction for kaggle competition (advanced regression, supervised ML)

Jupyter Notebook applying statistical theory to housing price prediction, using techniques like the Kolmogorov–Smirnov test, Box-Cox transformation, Pearson/Spearman correlations, chi-square tests, and feature importance, with analysis of prediction accuracy across price ranges.

A machine learning project that aims to predict the prices of homes listed in the Ames Housing Dataset based on their various features & attributes (via Kaggle's Competition)

Linear regression project using the Ames housing dataset to explore relationships between home features and sale price, including data cleaning, visualization, model training, evaluation, and interpretation in a clear, well-documented Jupyter notebook for learning and reference. ​

Prediction of Sales Prices of Houses

End-to-end ML pipeline that predicts house sale prices on the Ames Housing dataset using ZenML and MLflow. It ingests raw data, handles missing values and outliers, engineers features (log-transforming prices), splits into train/test, trains a Linear Regression model, tracks experiments and model versions with MLflow, and exposes a deployable predi