This project focuses on predicting the performance of servo motors based on various factors such as motor type, screw type, P-gain, and V-gain using machine learning regression models. The dataset used in this project contains observations of servo motor performance metrics.
- Project Overview
- Dataset
- Dependencies
- Installation
- Usage
- Exploratory Data Analysis (EDA)
- Machine Learning Models
- Evaluation Metrics
- Contributing
The dataset (servo.csv
) consists of the following columns:-
- Motor: Type of motor ('A', 'B', 'C', 'D', 'E')
- Screw: Type of screw ('A', 'B', 'C', 'D', 'E')
- Pgain: P-gain value (integer)
- Vgain: V-gain value (integer)
- Class: Performance class (integer)
This project requires the following dependencies:-
- Python 3.x
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
Install these dependencies using pip:
pip install pandas numpy scikit-learn matplotlib seaborn
To run the project locally, follow these steps:
Clone the repository:
git clone https://github.com/your-username/servo-motor-prediction.git
Navigate to the project directory:
cd servo-motor-prediction
Ensure all dependencies are installed (see Dependencies).
To use the project, follow these steps:-
- Ensure you have Python and the required libraries installed.
- Load the servo.csv dataset.
- Run the main script or Jupyter notebook for EDA and model training.
- Explore different models and evaluate their performance.
Explore the dataset to understand its structure and relationships between variables. Visualizations such as histograms, scatter plots, and heatmaps are used to gain insights into the data.
Train and evaluate different regression models:-
- Linear Regression
- Ridge Regression
- Decision Tree Regression
The performance of models is evaluated using Mean Squared Error (MSE), which measures the average squared difference between predicted and actual values. Lower MSE indicates better model performance.
MSE Results:-
- Linear Regression: 196.13
- Ridge Regression: 195.34
- Decision Tree Regression: 282.28
Based on MSE values, linear regression and ridge regression perform similarly and outperform decision tree regression.
This project aims to predict the occurrence of hills and valleys based on various factors using machine learning regression models. The dataset used in this project contains observations related to terrain profiles and their characteristics.
- Project Overview
- Dataset
- Dependencies
- Installation
- Usage
- Exploratory Data Analysis (EDA)
- Machine Learning Models
- Evaluation Metrics
- Contributing
The dataset (hill_valley.csv
) consists of the following columns:-
- V1, V2, ..., V100: Terrain profile features (numeric)
- Class: Binary indicator (0 or 1) for hill and valley classification
This project requires the following dependencies:-
- Python 3.x
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
Install these dependencies using pip:
pip install pandas numpy scikit-learn matplotlib seaborn
To run the project locally, follow these steps:
Clone the repository:
git clone https://github.com/your-username/hill-valley-prediction.git
Navigate to the project directory:
cd hill-valley-prediction
Ensure all dependencies are installed (see Dependencies).
To use the project, follow these steps:-
- Ensure you have Python and the required libraries installed.
- Load the hill_valley.csv dataset.
- Run the main script or Jupyter notebook for EDA and model training.
- Explore different models and evaluate their performance.
Explore the dataset to understand its structure and relationships between variables. Visualizations such as histograms, scatter plots, and heatmaps are used to gain insights into the data.
Train and evaluate different regression models:-
- Linear Regression
- Ridge Regression
- Decision Tree Regression
The performance of models is evaluated using appropriate metrics like accuracy, precision, recall, and F1-score for binary classification tasks.
- Linear Regression: Mean Squared Error = 0.30
- Ridge Regression: Mean Squared Error = 0.23
- Decision Tree Regression: Mean Squared Error = 0.49
Contributions are welcome! If you find any issues or have suggestions for improvement, please create a pull request or open an issue in the GitHub repository.