Calories Burnt Prediction using Machine Learning with Python

This project demonstrates a machine learning approach to predict the number of calories burnt during exercise based on various physiological and exercise-related parameters. The model is built using Python and various machine-learning libraries.

Click on the link below to access the deployed app: Run the App

Dataset

The dataset consists of two CSV files: calories.csv and exercise.csv.

Exercise Data

User_ID	Gender	Age	Height	Weight	Duration	Heart_Rate	Body_Temp
14733363	male	68	190.0	94.0	29.0	105.0	40.8
14861698	female	20	166.0	60.0	14.0	94.0	40.3
...	...	...	...	...	...	...	...

Calories Data

User_ID	Calories
14733363	231.0
14861698	66.0
...	...

The datasets are concatenated based on User_ID to form a comprehensive dataset for analysis.

Libraries Used

• streamlit: To create the web application.
• numpy: For numerical computations.
• pandas: For data manipulation and analysis.
• seaborn: For data visualization.
• matplotlib: For plotting graphs.
• scikit-learn: For building and evaluating machine learning models.
• xgboost: For implementing the XGBoost model.
• pickle: For saving the trained model.

Exploratory Data Analysis (EDA)

The following visualizations were used in the EDA:

1. Scatter plots to visualize relationships between features:

   • Height vs Weight
   • Duration vs Heart Rate
   • Duration vs Calories

2. Count plot of the Gender distribution.

3. Histograms to visualize the distribution of:

   • Age
   • Height
   • Weight

4. Correlation matrix heatmap to understand the relationships between numeric features.

Data Preparation

• The Gender column was mapped to numeric values (male: 0, female: 1).
• The data was split into training and testing sets using train_test_split.

Model Training

Four regression models were trained and evaluated:

• Linear Regression
• Random Forest Regressor
• Decision Tree Regressor
• XGBRF Regressor

The Random Forest Regressor was selected as the best model based on Mean Squared Error (MSE) and R² score.

Model Evaluation

Model	MSE	R² Score
Linear Regression	130.09	0.967
Random Forest Regressor	7.20	0.998
Decision Tree Regressor	28.62	0.993
XGBRF Regressor	58.36	0.985

The Random Forest Regressor was chosen due to its superior performance.

Model Verification

For model verification, check the ipynb file.

Application

Click on the link below to access the deployed app on your system: Run the App