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House Prediction Model

Overview

This project implements a House Prediction model using machine learning techniques. The goal is to predict house prices based on various features. The model is built using the RandomForest Regressor algorithm and utilizes popular data science libraries such as matplotlib, pandas, numpy, and seaborn.

Project Structure

|-- House_Prediction_Model.ipynb
|-- dataset.csv
|-- README.md
  • House_Prediction_Model.ipynb: Jupyter Notebook containing the code for the House Prediction model.
  • dataset.csv: CSV file containing the dataset used for training and testing the model.
  • README.md: Documentation providing information about the project, its structure, and how to use it.

Usage

  1. Open House_Prediction_Model.ipynb in Jupyter Notebook.
  2. Run the notebook cell by cell, following the instructions and comments in the code.
  3. The model will be trained and evaluated using the provided dataset.
  4. Make predictions using the trained model.

Dataset

The dataset (dataset.csv) contains the following columns:

  • Feature 1
  • Feature 2
  • ...
  • Target: House Price

Ensure the dataset is loaded correctly before running the model.

Model Evaluation

The model's performance is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Refer to the notebook for detailed information on model evaluation.

Visualizations

Matplotlib and Seaborn are used to create visualizations for better understanding and analysis of the dataset and model results.

Future Work

Future enhancements and improvements can include:

  • Hyperparameter tuning for better model performance.
  • Feature engineering to explore additional relevant features.
  • Deployment of the model for real-world predictions.

Feel free to contribute and expand on the project!