Real-estate-price-prediction

This GitHub repository provides a comprehensive implementation of linear regression for real estate price prediction. By leveraging a carefully curated dataset and applying linear regression techniques, this project aims to deliver accurate predictions for real estate prices, assisting buyers, sellers, and investors in making informed decisions.

Key Features

• Data Exploration and Preprocessing: Gain insights into the real estate dataset by performing exploratory data analysis (EDA) and preprocessing steps, including handling missing values, encoding categorical variables, and normalizing features.

• Feature Engineering: Utilize domain knowledge to engineer relevant features that can enhance the predictive power of the linear regression model, such as square footage, number of bedrooms, proximity to amenities, and more.

• Model Training and Evaluation: Implement and train a linear regression model using the processed dataset. Evaluate the model's performance using various metrics, including mean squared error (MSE), root mean squared error (RMSE), and R-squared.

• Model Interpretation: Examine the coefficients of the linear regression model to interpret the impact of each feature on the predicted real estate prices. Gain insights into which factors contribute the most to price fluctuations.

• Hyperparameter Tuning: Fine-tune the linear regression model by optimizing hyperparameters, such as learning rate, regularization parameters, and feature selection techniques, to improve prediction accuracy.

• Model Deployment: Explore options for deploying the trained linear regression model, enabling it to be used in real-world scenarios. Examples include developing a web application, creating APIs, or integrating the model into existing software systems.

Usage

1. Clone the repository:

git clone https://github.com/ahmedm-sallam/Real-estate-price-prediction.git

cd Real-estate-price-prediction

2. Install dependencies:

pip install -r requirements.txt

3. Explore the dataset:

• Take a look at the provided real estate dataset (data.csv) and understand the available features.
• Perform exploratory data analysis (EDA) to gain insights into the data distribution, correlations, and potential outliers.

4. Preprocess the data:

• Handle missing values and outliers appropriately.
• Encode categorical variables and normalize numerical features if required.
• Consider feature engineering techniques to create new features that can capture important aspects affecting real estate prices.

5. Train the linear regression model:

• Split the dataset into training and testing sets.
• Implement and train the linear regression model using the training data.
• Evaluate the model's performance on the testing data using relevant metrics.

6. Interpret the model:

• Analyze the coefficients of the linear regression model to understand the influence of each feature on the predicted real estate prices.
• Identify the most significant factors affecting price fluctuations.

7. Fine-tune the model:

• Experiment with different hyperparameter configurations, such as learning rate, regularization parameters, and feature selection techniques.
• Optimize the model's performance by tuning these hyperparameters.

8. Deploy the model:

• Explore various deployment options, such as developing a web application, creating APIs, or integrating the model into existing software systems.
• Ensure the trained model is easily accessible for making real estate price predictions.