It's a repository for my data science related tasks which i have done during oasis infobyte's data science internship.
Task-1: IRIS FLOWER CLASSIFICATION
Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Now assume that you have the measurements of the iris flowers according to their species, and here your task is to train a machine learning model that can learn from the measurements of the iris species and classify them.
It's a classification problem to solve it we can use model like Logistic Regression, Decision Tree, Random forest etc.
Task-2: UNEMPLOYMENT 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. 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.
TASK-3: CAR PRICE PREDICTION USING 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. Car price prediction is one of the major research areas in machine learning. So if you want to learn how to train a car price prediction model then this project is for you.
It is a regression problem, to solve it we can use model like Linear Regression, DecisionTreeRegresson etc.
Task-4: 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, use Python to build an email spam detector. Then, use machine learning to train the spam detector to recognize and classify emails into spam and non-spam. Let’s get started!
It is a Classification problem to solve it we can use Naive Bayes, Logistic Regression, Decision Tree, Random forest etc. before predicting we have to transform data to do this we have to use nltk library and have to do removing special characters, lowering text, tokenization, removing stopwords at last we have to do stemming after that we can use models.
Task-5: SALES PREDICTION
Sales prediction means predicting how much of a product people will buy based on factors such as the amount you spend to advertise your product, the segment of people you advertise for, or the platform you are advertising on about your product.
Typically, a product and service-based business always need their Data Scientist to predict their future sales with every step they take to manipulate the cost of advertising their product. So let’s start the task of sales prediction with machine learning using Python.
It's a regression problem so we can use any regression model here I used Linear regression and K nearest neighbor.
From which I have done Task 1, 3, 4 and 5.