This project implements a web application and uses a machine learning model to predict house prices in California based on various features.
The dataset used for training and testing is sourced from the California housing data, containing information such as housing median age, total rooms, total bedrooms, population, etc.
- You can find the dataset here
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Data Exploration: Explore and visualize the California housing dataset to gain insights into the data distribution and relationships between features.
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Data Preprocessing: Handle missing values, scale features, and preprocess the data for training machine learning models.
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Machine Learning Model: Train a regression model to predict house prices using popular algorithms such as Linear Regression, Decision Trees, and Random Forests.
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Evaluation: Evaluate the model's performance using appropriate metrics, and fine-tune the model for better predictions.
The model was saved using .pkl file and web application was created using Flask to interact with model by providing the features to get the estimated price of the house.