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TAMS Artificial Intelligence Society Summer Competition Notebooks and Files; won honorable mention.

This Python notebook is a simple housing price prediction model using linear regression.

This is a notebook of house price predictions based on a dataset of a Kaggle competition.

Machine learning model to predict house price using linear regression and iris flower classification on jupyter notebook

Stores the basic Machine Learning models that I've worked on and learned from using Kaggle, Google Colab and Jupyter Notebook

This notebook is about data visualization, pre-processing the data and selecting regression model out of different regression model based on the accuracy given on validation data.

This ipython notebook is working to build a model which will predict the house price based on some features, identify the features that affects the house’s price and carry out evaluation matrices.

This repository contains two machine learning projects focused on different aspects of predictive modeling. Each project is implemented using linear regression and logistic regression and includes Jupyter notebook code for easy understanding and reproduction.

This repository showcases a collection of practical machine learning projects, utilizing diverse datasets for tasks like classification, regression, and forecasting. With Jupyter notebooks detailing techniques from decision trees to neural networks, this project demonstrates my ability to solve real-world problems through data-driven insights.

Before GUI, There are Two ways to preprocessing any data set with two jupyter notebooks, GUI to choose Cleaned CSV data_set,Show most of properties of this data_set,Choose test size & alpha size & error metrics to train Ml algorithm on this data set,show ( test & train ) Percentage as output

In this notebook, I have done data analysis on house price prediction problem

House Price Detection-Advanced Regression (Neural Network)