AI & Python #35: Data Science Projects in Python for All Levels
Beginner and advanced data science projects with source code.
Hi!
Before we start with today’s article, I’d like to tell you about something that might be of interest to those interested in Python and data science. DataCamp has an incredible certification program that’s perfect for you: The Python Data Associate Certification.
This certification is designed to help you:
Prove your Python skills are ready for real-world application
Build credibility in the competitive data industry
Land your dream job in the data field
If you’re serious about standing out in the job market, this certification is the perfect step forward. To get the certification, you need to register, and then you’ll have 30 days to conquer a timed exam and practical exam. If you need to give it a second try, Datacamp can help you fill the gaps in your knowledge before you try again.
What I like about Datacamp is that they offer interactive lessons, practical exercises, and real-world projects. Projects are very useful to put the concepts we learn into practice, and today we’ll see 5 data science projects you can do with Python.
I love solving projects to learn data science and Python.
Why? A project will help you put into practice all the knowledge you’ve acquired from math, statistics, and programming. It’s like putting the pieces of a puzzle together to solve a real-world problem.
In this article, I listed some data science projects you can do with Python. The projects are listed by difficulty starting with the beginner projects. Knowledge of libraries such as Pandas, Numpy, and Scikit-learn is necessary to solve most of these projects. I’ll leave links to tutorials to help you solve these projects.
1. Sentiment Analysis
The first project of this list is to build a machine-learning model that predicts the sentiment of a movie review. Sentiment analysis is an NLP technique used to determine whether data is positive, negative, or neutral. It’s really helpful for businesses because it helps them understand the overall opinions of their customers.
For this project, you will use an IMDB dataset that contains 50k movie reviews. with 2 columns (review and sentiment). The goal is to build the best machine learning model that predicts the sentiment given a movie review.
To make this project beginner friendly you only have to predict whether a movie review is positive or negative. This is known as binary text classification because there are only two possible outcomes.
One of the things that make this first project special is that you will explore the scikit-learn library while building a basic machine-learning model from scratch.
Libraries: Pandas, Scikit-learn
Tutorial: Sentiment Analysis in Python (Text Classification)
2. Fake News Detection
The most beginner-friendly detection project is probably Fake News Detection. Fake news is spread everywhere on the internet. This generates confusion and panic among the population. This is why is important to identify the authenticity of the information. Fortunately, we can use Python to tackle this data science project.
Libraries: Scikit learn (TfidfVectorizer and PassiveAggressiveClassifier), Pandas and Numpy
Tutorial: Detecting Fake News
The goal of this project is to separate real news from fake news. To do so, we will use sklearn’s tools such as TfidfVectorizer and PassiveAggressiveClassifier.
3. Credit card fraud detection
Credit card fraud costs both consumers and companies billions of dollars while fraudsters keep trying to find new ways to commit these illegal actions. This is why fraud detection systems have become essential for banks to minimize losses.
In this project, you should analyze customer’s spending behavior from a dataset that contains transaction history. Variables like the location will help you identify fraudulent transactions.
Libraries: Pandas, Numpy, Matplolib, Scikit-learn, Machine Learning Algorithms (XGBoost, Random forest, KNN, Logistic regression, SVM, and Decision tree )
Source Code: Credit Card Fraud Detection With Machine Learning in Python
4. Chatbots
A chatbot is just a program that simulates human conversation through voice commands or text chats. Advanced chatbots are built using artificial intelligence and used in most messaging applications you have on your phone.
Although creating voice assistants like Siri and Alexa are too complex, we can still create a basic chatbot using Python and deep learning. In this project, you will have to train a chatbot with a dataset using data science techniques. As these chatbots process more interactions, their intelligence and accuracy will increase.
Building a simple chatbot will expose you to a variety of useful skills for data science and programming
5. Customer Churn Prediction
Customer churn is the rate at which customers stop doing business with a company. This represents the percentage of subscribers who discontinue their subscriptions within a given time period. This is a good project to test your data science skills. I even had to solve it in hackathons!
The main goal of this project is to classify if a customer is going to churn or not. To do so, you will use a dataset that has financial data about a bank’s customers. Information such as credit score, tenure, number of products, and estimated salary will be used to build this prediction model.
This project and the credit card fraud detection project are the most complete data science projects listed in this article. It includes exploratory data analysis, feature engineering, data preparation, model fitting, and model selection.
Libraries: Pandas, Matplolib, Scikit-learn, Machine Learning Algorithms (XGBoost, Random forest, KNN, Logistic regression, SVM, and Decision tree)
Source Code: Bank Customer Churn Prediction
Good luck solving these projects!