This project aims to predict property prices based on various features such as location, area, number of bedrooms, and other factors. It uses machine learning regression techniques to build a model that estimates the price of a property based on these factors.
- Python
- Pandas (for data manipulation)
- NumPy (for numerical operations)
- Matplotlib & Seaborn (for data visualization)
- Scikit-learn (for machine learning algorithms)
The dataset used in this project contains information about properties, such as:
- Area (in square feet)
- Location (categorical data)
- Number of bedrooms
- Age of the property
- Other relevant features
The dataset is typically stored in a CSV format, and preprocessing steps are done to handle missing values, encode categorical variables, and scale numerical features.
To get started with the project, follow these steps:
-
Clone this repository:
git clone https://github.com/swastiswagat/Internshala-Machine-Learning.git cd Internshala-Machine-Learning -
Install the necessary dependencies:
pip install -r requirements.txt
Prepare the dataset: Place the dataset in the data/ directory or adjust the file path in the code accordingly. Run the script to train the model and make predictions:
python Property\ Price\ Prediction.ipynb