Machine Learning Project with Streamlit This repository contains a machine learning project that predicts housing prices using a regression model trained on a dataset of real estate listings. The project is written in Python with Streamlit for web deployment.
The project consists of the following steps:
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Data cleaning: The dataset is preprocessed to handle missing values, outliers, and other data quality issues.
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Preprocessing: The dataset is transformed into features that can be used by the regression model. This includes encoding categorical variables, scaling numerical variables, and creating new features.
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Model training: The regression model is trained using a training dataset. We evaluate multiple regression algorithms and select the best performing one.
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Model testing: The trained model is tested using a testing dataset to evaluate its performance. We use metrics such as mean absolute error and mean squared error to assess the model's accuracy.
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Web deployment: The model is deployed using Streamlit, a Python library for building web applications. The user can input the features of a house and the model will output a predicted housing price. The user can also visualize the data used for training and testing the model.
This project demonstrates the application of machine learning techniques to real estate data. The regression model trained on this data can be used to predict housing prices with reasonable accuracy. The web deployment of the model using Streamlit makes it accessible to a wider audience.