This repository contains a web app developed using Streamlit and hosted on Streamlit Cloud. The web app integrates six different regression projects, each utilizing machine learning models to provide accurate predictions. The projects covered are:
- House Price Prediction
- Car Price Prediction
- Gold Price Prediction
- Medical Insurance Cost Prediction
- Big Mart Sales Prediction
- Calories Burnt Prediction
- Overview
- Installation
- Usage
- Dataset Description
- Technologies Used
- Model Development Process
- Models Used
- Model Evaluation
- Conclusion
- Deployment
- Contributing
- Contact
This web application allows users to select from six different regression projects and get predictions based on the input features. Each project was developed through extensive data analysis and model selection processes, ensuring high accuracy and reliability in predictions.
To run this project locally, please follow these steps:
- Clone the repository
- Navigate to the project directory
- Install the required dependencies
git clone <repository_url>
cd <project_directory>
pip install -r requirements.txtTo start the Streamlit web app, run the following command in your terminal:
streamlit run streamlit_app.pyThis will launch the web app in your default web browser. You can select the desired regression project from the sidebar and input the required features to get a prediction.
Description: Predicts house prices based on features such as location, square footage, number of bedrooms, and other property details.
Description: Predicts car prices based on attributes like brand, model, year of manufacture, mileage, and engine specifications.
Description: Predicts the price of gold based on historical data, including currency exchange rates, inflation rates, and global financial indicators.
Description: Predicts insurance premiums based on features like age, BMI, smoking status, and medical conditions.
Description: Predicts sales for various items in different stores based on factors such as store type, item visibility, and marketing data.
Description: Predicts the number of calories burnt based on physical activities, age, weight, and duration of exercise.
- Programming Language: Python
- Web Framework: Streamlit
- Machine Learning Libraries: Scikit-learn, XGBoost
- Data Analysis and Visualization: Pandas, NumPy, Matplotlib, Seaborn
Each classification project was developed through the following steps:
- Importing the Dependencies
- Exploratory Data Analysis (EDA)
- Data Preprocessing
- Handling missing values
- Handling outliers
- Label encoding/One-hot encoding
- Standardizing the data
- Model Selection
- Selected the most common 5 regression models
- Trained each model and checked cross-validation scores
- Chose the top 3 models based on cross-validation scores
- Model Building and Evaluation
- Selected best features using Recursive Feature Elimination (RFE)
- Performed hyperparameter tuning using Grid Search CV
- Built the final model with the best hyperparameters and features
- Evaluated the model using mean squared error, R-squared score, and other relevant metrics
The top 3 models for each classification project are as follows:
- Linear Regression: Simple and interpretable.
- Random Forest Regressor: Effective for high-dimensional data.
- XGBoost Regressor: Known for its high performance.
- Linear Regression: Simple and interpretable.
- Random Forest Regressor: Effective for high-dimensional data.
- XGBoost Regressor: Known for its high performance.
- K-Nearest Neighbour: Simple algorithm that works well with small datasets.
- Random Forest Regressor: Effective for high-dimensional data.
- XGBoost: Boosting algorithm known for high performance.
- K-Nearest Neighbour: Simple algorithm that works well with small datasets.
- Random Forest Regressor: Effective for high-dimensional data.
- XGBoost: Boosting algorithm known for high performance.
- Linear Regression: Simple and interpretable.
- Lasso Regression: Linear model with L1 regularization to handle multicollinearity and feature selection effectively.
- XGBoost: Powerful gradient boosting framework.
- K-Nearest Neighbour: Simple algorithm that works well with small datasets.
- Random Forest Classifier: Ensemble method that reduces overfitting.
- XGBoost: Powerful gradient boosting framework.
- Linear Regression: R² = 0.99999
- Random Forest Regressor: R² = 0.99999
- XGBoost: R² = 0.99998
- Random Forest Regressor: R² = 0.66816
- XGBoost: R² = 0.63702
- Linear Regression: R² = 0.47672
- XGBoost: R² = 0.98654
- Random Forest Regressor: R² = 0.98880
- K Neighbors Regressor: R² = 0.98934
- Linear Regression: R² = 0.86308
- XGBoost: R² = 0.86308
- Random Forest Regressor: R² = 0.87258
- XGBoost: R² = 0.61418
- Lasso Regression: R² = 0.57886
- Linear Regression: R² = 0.57842
- Random Forest Regressor: R² = 0.99847
- XGBoost: R² = 0.99935
- K Neighbors Regressor: R² = 0.99513
This ML Regression Projects WebApp provides an easy-to-use interface for predicting various outcomes based on input features. The models used are well-validated and tuned for high accuracy. The system aims to assist in decision-making and prediction tasks across different domains.
The web app is hosted on Streamlit Cloud. You can access it using the following link:
Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.
If you have any questions or suggestions, feel free to contact me at [email protected].