HOUSE-PRICE-PREDICTION-MODEL

This project uses machine learning to predict house prices based on features like area, bedrooms, and location.

Data

• Dataset: Housing.csv
• Contains information about houses, including area, bedrooms, bathrooms, location, etc.

Steps

1. Data Loading and Cleaning:
   • Loaded the dataset using pandas.
   • Converted categorical variables into numerical representations.
2. Exploratory Data Analysis:
   • Analyzed the relationship between features and price using correlation and box plots.
3. Feature Selection and Engineering:
   • Selected relevant features for the model.
4. Model Building:
   • Split the data into training and testing sets (80% train, 20% test).
   • Trained a Linear Regression model, Support Vector Machine and Decision Tree.
5. Evaluation:
   • Evaluated the model using the R-squared metric.
   • Calculated model coefficients and intercept.

Results

• The Linear Regression model achieved an accuracy of 60% on the test set.
• Other models were also explored (Support Vector Machine, Decision Tree) but results are not included in this README.

Usage

To use this notebook, upload your Housing.csv file to Google Colab and run the code cells sequentially. You can modify the code to experiment with different models and parameters.