Have you ever been overwhelmed by the amount of data science concepts and projects present on the internet and did not know where to start? If you have ever felt like that, don’t worry, you have come to the right place because we have got you covered.
6 Fun DataScience Projects To Learn Python
We know that data science is an emerging field, and there is unlimited content regarding data science on the internet. But we also know that beginners in data science want to start from scratch, and that is why in this article we have compiled some of the data science projects at the beginner level as well as some that are a little bit on the advanced side. So let’s get you started on your data science journey!
1. Breast Cancer Classification
Breast cancer is one of the most common and prevalent cancers in women. For the past few decades, machine learning techniques have been extensively used for healthcare applications and especially for breast cancer diagnosis and prognosis.
As we know, early detection of cancer can help patients get the proper treatment on time and also increase their chances of survival. Also, the proper identification of the tumor type can prevent the patient from going through a futile treatment process.
You can make use of the Naive Bayes Algorithm in machine learning for this type of classification project. You can use the dataset of breast cancer provided by Scikit-learn or you can use datasets from Kaggle for breast cancer classification.
Note: To evaluate the performance of your model, you will need to test the model on invisible data. Split your dataset in the ratio of 80:20 to create a training set and a test set. You can check the accuracy of your model using the accuracy_score() function from Scikit-learn.
2. Car Price Prediction
You can create a model for car price prediction using the Linear Regression model with PyTorch.PyTorch is a very flexible library in python which is used for building deep-learning models. This project will help you in strengthening your concept of building deep-learning models.
Before starting to build your model, make sure you clean your dataset which means filtering your data and dropping the columns which do not significantly contribute to the prediction. Also, keep in mind for this project, you are using PyTorch, so for using the data for training, you need to convert the data frame into PyTorch sensors.
For that, first, the input and the output columns should be converted into NumPy arrays and then the NumPy arrays should be converted into PyTorch tensors. After that, you can move on to build a linear regression model using PyTorch.
3. Fake News Detection
Fake news is all over the internet these days. A countless number of news media and news houses have opened nowadays and with the easy access to the internet, it has become easier for these media houses to publish fake news. A fake news detection model can help us detect fake news and remove it from the internet.
You can use a logistic regression model for this project. You can train and test your model using a logistic regression algorithm. As a part of the data cleaning, remove the missing values and merge all text together.
4. Chatbot with Machine Learning
Did you know you can make your own chatbot using Machine learning? How cool right! You either can download a dataset for this or you can make your own dataset. Depending on which domain you want to build your chatbot, you first need to understand the intentions of your chatbot, and based on those intents you are going to train your data.
For making your own dataset, it is necessary that you understand the intentions of a user or how the user may interact with the chatbot, or what questions the user might ask the chatbot.
For the chatbot to continue answering to the users, it is vital that it understands the real intention of the users behind those messages. You have to play with a little bit of strategy here. You have to create different intents and have form training samples for each of these intents. Then your chatbot model will be trained on the sample training data you have created.
5. Air Quality Index Analysis
Air Quality Index is often used by government agencies to indicate the level of air pollution or the health risk that may be present due to the particulate matter in the air. It is expressed from the range of 0-500. An AQI value of less than or equal to 100 is considered as good.
There are six categories in AQI to indicate different categories of health problems. For this project, you need to first visualize and understand the significance of each color in AQI. The color indicates the air quality and how harmful it is in each region. Although this project is a bit on the advanced side, it will give you the extra edge in your data science journey.
6. Sentiment Analysis in Python
Sentiment analysis is a method by which you analyze a piece of text to understand the sentiment hidden within it. In other words, it allows you to determine the feelings in a piece of text. In this process, you will use both machine learning and NLP techniques. For this project, you need to build a binary text classifier to understand the sentiment behind it.NLP techniques will be used to clean the data and to build the text classifier with LSTM layers.
To get hands-on working experience on many interesting real-world Data Science projects such as Sales Growth Dashboard, Real Estate Investment, etc., you can join a Data Science Bootcamp.
This boot camp offers complete mentorship by industry experts throughout all the projects. These projects can help your portfolio stand out from the competition and can also provide you with industry exposure.
In this article, you learned about some of the potential data science projects in python that will help you boost your data science portfolio and your data science knowledge. Work out these projects with the datasets provided and try to analyze the results from it and draw insights from the data.