https://github.com/praveen-334/house-price-prediction-using-linear-regression

https://github.com/praveen-334/house-price-prediction-using-linear-regression

eda linear-regression machine-learning matplotlib-pyplot pandas-dataframe python3 seaborn sklearn

Last synced: 18 days ago
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• Host: GitHub
• URL: https://github.com/praveen-334/house-price-prediction-using-linear-regression
• Owner: praveen-334
• Created: 2024-07-25T16:22:42.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-18T14:42:36.000Z (5 months ago)
• Last Synced: 2024-11-10T04:13:48.836Z (about 2 months ago)
• Topics: eda, linear-regression, machine-learning, matplotlib-pyplot, pandas-dataframe, python3, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.18 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# House Price Prediction using Linear Regression

This project uses a linear regression model to predict house prices based on various features such as number of bedrooms, bathrooms, square footage, etc. The model is trained on the house price data provided in the house-price-data.csv file.

## Dependencies

Python 3.x

NumPy

Pandas

Scikit-learn

# Data preprocessing

### 1 . Load the CSV file:

```python

  data = pd.read_csv('house-price-data.csv')

```

### 2 . Handle missing values (if any) and perform feature engineering as needed.

### 3 . Split the data into training and test sets:

```python

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=42)

```

# Model Training

### 1 . Create and train the linear regression model:

```python

from sklearn.linear_model import LinearRegression

model = LinearRegression()

model.fit(X_train, y_train)

```

### 2 . Evaluate the model's performance on the test set:

```python

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)

r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse:.2f}')

print(f'R-squared: {r2:.2f}')

```

# Future Improvements

Explore more advanced regression techniques, such as polynomial regression or regularized models, to potentially improve the model's performance.

Conduct feature selection to identify the most important predictors of house prices.

Gather additional data sources to enrich the feature set and capture more aspects that influence house prices.

https://github.com/praveen-334/house-price-prediction-using-linear-regression/pull/1#issue-2430583429