This repository consists of Data Science Projects that i have done during my Internship at Oasis Infobyte.

NAME: IRIS FLOWER CLASSIFICATION
Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Here the task is to train a machine learning model that can learn from the measurements of the iris species and classify them.
TECHNOLOGIES USED: Python Programming, Kaggle Notebook, Scikit-learn dataset.

NAME: UNEMPLOYMENT RATE ANALYSIS
Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. Here the task is to analyze the increase in the unemployment rate during COVID-19.
TECHNOLOGIES USED: Python Porgramming, Kaggle Notebook, Unemployment_Rate_Datsaset.csv dataset.

NAME: CAR PRICE PREDICTION
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. Car price prediction is one of the major research areas in machine learning.
TECHNOLOGIES USED: Python Porgramming, Kaggle Notebook, car data.csv dataset.

NAME: EMAIL SPAM DETECTION
We’ve all been the recipient of spam emails before. Spam mail, or junk mail, is a type of email that is sent to a massive number of users at one time, frequently containing cryptic messages, scams, or most dangerously, phishing content. In this Project, using Python i built an email spam detector. Then, used machine learning to train the spam detector to recognize and classify emails into spam and non-spam.
TECHNOLOGIES USED: Python Porgramming, Kaggle Notebook, mail_data.csv dataset.

Kaggle notebooks are a feature provided by Kaggle, a popular platform for data science and machine learning competitions. These notebooks are based on Jupyter Notebooks and provide a web-based environment for data scientists and analysts to write, run, and share code. Kaggle notebooks support various programming languages, including Python and R.

1. Interactive Environment
2. Integrated Datasets
3. Version control
4. Sharing and Collaboration
5. Access to GPU and TPU
6. Markdown Support
7. Kaggle Kernels