House-Rent-Predicition

Dataset Overview

Objective

The project aims to build a machine‑learning model that estimates rental prices for houses or apartments. The model will take property‑related feature inputs and output an estimated rent cost.

Data Processing Steps

Data Collection

• Gather a comprehensive dataset of rental properties covering features such as bedroom count, bathroom count, area, location, amenities, and more.

Data Cleaning and Pre‑processing

• Handle missing values, outliers, and inconsistencies.
• Encode categorical variables and preprocess numeric features.
• Explore relationships among features.

Feature Engineering

• Extract relevant features from the dataset.
• Create new features that may improve model predictive power.

Data Splitting

• Split the dataset into training and testing sets for accurate performance evaluation.

Model Selection

• Choose regression models suitable for rent price prediction, such as linear regression, decision trees, random forests, or gradient boosting.

Model Training

• Train the selected model on the training data.
• Tune hyper‑parameters to optimize performance.

Model Evaluation

• Evaluate the model on the test set.
• Use metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R‑squared to assess accuracy.

Deployment (Optional)

• If needed, deploy the trained model as a web service or API for user interaction and rent predictions.