House Price Prediction project aims to develop a predictive model using Random Forest to estimate the price of house. The real estate market has always been a fascinating field, and with the help of data science techniques, we can now make more accurate predictions about house prices. Leveraging the Random Forest algorithm, which combines the power of multiple decision trees, we can capture complex relationships and important features that influence house prices.
The model takes into account various factors such as location, square footage, number of bedrooms, bathrooms, and other significant attributes to estimate the price of a house. It has been trained on a comprehensive dataset, ensuring that it provides reliable predictions that align with market trends.
Data Collection: Kaggle Data link: https://www.kaggle.com/datasets/harishkumardatalab/housing-price-prediction
Data Preprocessing: Cleaning and preprocessing the dataset to handle missing values, outliers, and inconsistencies. This step also involves transforming categorical variables into numerical representations, normalizing numeric features, and splitting the dataset into training and testing subsets.
Model Training: Implementing linear regression using appropriate libraries or frameworks. The training process involves fitting the model to the training data, estimating the coefficients (slope and intercept), and optimizing the model's performance by minimizing the residual errors between the predicted and actual values.
Model Evaluation: Assessing the performance of the trained linear regression model using evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared. These metrics provide insights into how well the model predicts the values and indicate its overall accuracy.
Prediction and Deployment: in this phase, I've used Streamlit for deploying the app. to predict the values for house which is not included in the training phase.
If you found the House Price Prediction project helpful and insightful, I would also greatly appreciate an upvote on Kaggle. Your support will contribute to its visibility and encourage others to benefit from this project. 🙏👍 Kaggle: https://lnkd.in/gxQ69ctM
Once the Streamlit application is running, you will be presented with a user interface containing input fields for various features. Enter the relevant details, such as location, square footage, number of bedrooms, bathrooms,etc. and click on the "Predict" button. The application will utilize the trained Random Forest model to generate a predicted price for the house based on the provided features.
Contributions to this project are welcome. If you would like to contribute, please follow these steps:
- 1)Create a new branch from the
mainbranch to work on your changes. - 2)Make your modifications and commit your changes.
- 3)Push your branch to your forked repository.
- 4)Open a pull request to the original repository, describing the changes you made.
This project is licensed under the GPU License.
- The dataset used in this project is sourced from: https://www.kaggle.com/datasets/harishkumardatalab/housing-price-prediction
- The Random Forest algorithm is implemented using the scikit-learn library.
- The Streamlit framework is used for creating the web application.
If you have any questions or suggestions regarding this project, please feel free to contact me at 132anaskhan@gmail.com.