This project builds a Machine Learning model to predict house prices based on various features such as area, number of bedrooms, and other property-related attributes.
The goal is to understand how different factors influence house prices and create a predictive model using Linear Regression.
- File:
housing.csv - Rows: ~500+
- Columns: Multiple
areabedroomsbathroomsstoriesparkingprice(Target Variable)
- Python 🐍
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
- Loaded dataset using Pandas
- Checked structure, shape, and data types
- Checked for missing values
- Handled inconsistencies (if any)
- Converted categorical variables into numerical format (if required)
- Selected relevant features for prediction
- Encoded categorical variables
- Split data into:
- Training set
- Testing set
- Used Linear Regression algorithm
- Trained model on training data
- Predicted house prices on test data
Evaluated model performance using:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- R² Score
These metrics help in understanding the accuracy and reliability of the model.
- 🏠 Area is the most influential factor in determining house prices
- 🛏️ More bedrooms and bathrooms generally increase price
- 🚗 Parking availability positively impacts price
- 📈 Strong linear relationship between features and price
The notebook includes:
- Scatter plots 📉
- Regression plots 📈
- Heatmaps 🔥
These visualizations help in identifying relationships between variables and price trends.
git clone https://github.com/Dev1822/Heart-Disease-EDA
cd Heart-Disease-EDA
pip install pandas matplotlib seaborn
Made By : https://github.com/Dev1822