https://github.com/suhaanigurjar/cbt-cip
CipherByte Technologies Internship Project
https://github.com/suhaanigurjar/cbt-cip
Last synced: about 1 month ago
JSON representation
CipherByte Technologies Internship Project
- Host: GitHub
- URL: https://github.com/suhaanigurjar/cbt-cip
- Owner: suhaanigurjar
- Created: 2024-07-12T16:32:38.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-09-29T16:04:51.000Z (8 months ago)
- Last Synced: 2025-03-30T11:11:10.392Z (2 months ago)
- Language: Python
- Size: 37.1 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Science Projects
This repository contains two projects developed as part of the Data Science Internship at CipherByte Technologies:
1. **Iris Flower Classification Model**: A machine learning project focused on classifying Iris flowers based on their physical measurements.
2. **Time Series Forecasting**: A project for analyzing and predicting trends in time series data.
Below is a detailed overview of the **Iris Flower Classification** project.
---
## Iris Flower Classification Model
### Data Science Internship - CipherByte Technologies
This project involves training a machine learning model to classify Iris flowers into one of three species: **Setosa**, **Versicolor**, or **Virginica**. The classification is based on the measurements of the flowers' sepals and petals.
### Task Overview
The Iris Flower Classification task includes:
- Data collection and preprocessing
- Visualizing the data to understand the distribution of features
- Training a Logistic Regression model on the Iris dataset
- Evaluating model performance using accuracy, classification report, and cross-validation
- Predicting the species of Iris flowers based on new data inputs
### Dataset Description
The Iris dataset consists of the following features:
- **Sepal Length (cm)**
- **Sepal Width (cm)**
- **Petal Length (cm)**
- **Petal Width (cm)**
- **Species** (Target variable: Setosa, Versicolor, Virginica)
### Libraries and Tools Used
- **Pandas**: For data manipulation and analysis
- **Seaborn** and **Matplotlib**: For data visualization
- **Plotly**: For interactive scatter plot visualizations
- **Scikit-learn**: For model training, evaluation, and metrics
## Project Structure
```bash
├── IrisFlowerClassification.ipynb # Jupyter Notebook with the complete code
├── Iris_Flower_Data.csv # Dataset
├── TimeSeriesForecasting.ipynb # Time Series Forecasting project
└── README.md # Project documentation
```
## Results
The model is evaluated based on:
- **Accuracy**: Measures the proportion of correct predictions.
- **Confusion Matrix**: Analyzes how well the model classifies each species.
- **Cross-Validation**: Ensures model stability across different subsets of the dataset.
## Conclusion
This Iris Flower Classification project successfully demonstrates how to build, train, and evaluate a machine learning model using the Logistic Regression algorithm for classification tasks.
---
For details on the **Time Series Forecasting** project, please refer to the `TimeSeriesForecasting.ipynb` file in the repository.
## Acknowledgments
This project was developed as part of the Data Science Internship at CipherByte Technologies.