https://github.com/prathuashakb/oasis-infobyte-internship
A collection of projects that I have worked on to complete the Oasis Infobyte Internship in the data science domain.
https://github.com/prathuashakb/oasis-infobyte-internship
data-science internship-task machine-learning oasis-infobyte
Last synced: about 2 months ago
JSON representation
A collection of projects that I have worked on to complete the Oasis Infobyte Internship in the data science domain.
- Host: GitHub
- URL: https://github.com/prathuashakb/oasis-infobyte-internship
- Owner: PrathuashaKB
- Created: 2023-08-04T05:55:54.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2025-04-03T10:29:40.000Z (about 1 year ago)
- Last Synced: 2025-04-03T11:29:33.420Z (about 1 year ago)
- Topics: data-science, internship-task, machine-learning, oasis-infobyte
- Language: Jupyter Notebook
- Homepage:
- Size: 3.11 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Oasis Infobyte
I had the opportunity to be part of [OASIS INFOBYTE](https://oasisinfobyte.com/), a dynamic and inclusive community dedicated to empowering students through learning and leadership development. It’s a platform that brings together individuals from diverse backgrounds who share a common goal of growth and innovation. At Oasis Infobyte, the focus is on creating opportunities for real-world learning, student engagement, and fostering shared interests by working on various tasks from their curated [task list](https://www.scribd.com/document/765230528/AICTE-OASIS-INFOBYTE-SIP-TASK-LIST)
# Oasis Infobyte Data Science Internship
This repository contains 3 tasks that were completed successfully for the Data Science Internship at Oasis Infobyte.
## TASK 1
### IRIS FLOWER CLASSIFICATION
Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. With the measurements of the iris flowers according to their species we can train a model that can learn from the measurements of the iris species and classify them accordingly with Machine Learning.
#### Dataset :
Iris dataset from **Kaggle** : [DATASET](https://www.kaggle.com/datasets/saurabh00007/iriscsv)
#### Project Steps :
1. Data Preprocessing : Load the Iris dataset and inspect its structure.
2. Data Analysis : Explore the dataset by visualizing the relationships between different features.
3. Data Splitting : Split the dataset into training and testing sets.
4. Model Training : Select multiple machine learning algorithms suitable for classification tasks, train each model using the training data.
5. Model Evaluation : Evaluate the trained models using the testing data.
6. Model Deployment : Using the Streamlit library to create a web-based GUI for the Iris flower classification model.
#### UI for Iris flower classification :
#### Repository : [OIBSIP_TASK1](https://github.com/PrathuashaKB/Oasis-Infobyte-Internship/tree/main/OIBSIP_TASK%201)
## TASK 2
### CAR PRICE PREDICTION
Car price prediction is one of the major research areas in machine learning. The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives and many more. By considering these features we can train a model that can learn from the given data and make predictions accordingly with Machine Learning.
#### Dataset :
Car dataset from **Kaggle** : [DATASET](https://www.kaggle.com/code/goyalshalini93/car-price-prediction-linear-regression-rfe)
#### Project Steps :
1. Data Preprocessing : Load the car dataset and inspect its structure.
2. Data Analysis : Explore the dataset by visualizing the relationships between different features.
3. Data Splitting : Split the dataset into training and testing sets.
4. Model Training : Select multiple machine learning algorithms suitable for classification tasks, train each model using the training data.
5. Model Evaluation : Evaluate the trained models using the testing data.
6. Model Deployment : Using the Streamlit library to create a web-based GUI for the car price prediction model.
#### UI for Car price prediction :
#### Repository : [OIBSIP_TASK2](https://github.com/PrathuashaKB/Oasis-Infobyte-Internship/tree/main/OIBSIP_TASK%202)
## TASK 3
### UNEMPLOYMENT ANALYSIS WITH PYTHON
Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. We have seen a sharp increase in the unemployment rate during COVID-19, so analyzing the unemployment rate can be a good data science project.
#### Dataset :
Unemployment dataset from **Kaggle** : [DATASET](https://www.kaggle.com/datasets/gokulrajkmv/unemployment-in-india)
#### Project Steps :
1. Data Preprocessing : Load the unemployment in India dataset and inspect its structure.
2. Data Analysis : Explore the dataset by visualizing the relationships between different features.
3. Data Splitting : Split the dataset into training and testing sets.
4. Model Training : Select multiple machine learning algorithms suitable for classification tasks, train each model using the training data.
5. Model Evaluation : Evaluate the trained models using the testing data.
#### Repository : [OIBSIP_TASK3](https://github.com/PrathuashaKB/Oasis-Infobyte-Internship/tree/main/OIBSIP_TASK%203)
#### I express my gratitude to Oasis Infobyte for the opportunity to exhibit my abilities & skills as a data science intern.
#### Prathuasha K B