5 Solved end-to-end Data Science Projects in Python
Beginner and advanced Python data science projects with source code.
If you’ve been studying data science for a while, you might know that in order to learn data science you need to learn math, statistics, and programming. This is a good start for anyone interested in data science, but do you know how to get even more exposure to data science?
It’s with projects! A project will help you put into practice all the knowledge you’ve acquired from math, statistics, and programming. So far you might’ve seen each of them individually, but after you finish a project, the concepts you’ve learned in each field will make more sense.
In this article, I listed some end-to-end data science projects you can do with Python. The projects are listed by difficulty, so the beginner projects are in the beginning, while the advanced projects are at the end of the article.
Note: Most projects listed in this article require a fair knowledge of Python. You should at least know the basics of libraries such as Pandas, Numpy, and Scikit-learn, etc. I’m going to leave the source code of each project as well as a guide of the libraries used in each project. If you are still a beginner in Python, I recommend you start with basic Python projects first.
First Things First — Learn Exploratory Data Analysis
Most real-world projects you will solve in the future as well as some projects listed in this article will require you to perform an EDA (exploratory data analysis). This step is essential in every data science project because it helps you make sense of your data and obtain useful insights with visualization techniques.
EDA also helps to expose unexpected results and outliers in your data. For example, graphs like histogram, boxplot, and barplot will help you identify outliers, so you can get rid of them and perform a better analysis.
I’m not counting EDA as a project in this list because it’s usually not the final project’s goal, but something you must do in order to perform a better analysis. To learn how to perform EDA, check this guide that will introduce you to data visualization in Python. In the guide, you will have to obtain insight from a dataset that contains football players' stats. Also, check this other guide to learn the best practices of data cleaning in Python. This second guide will show you how to identify and deal with outliers using the plots you learned in the first guide.
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 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.
- Libraries (guides included): Pandas, Scikit-learn
- Source Code: Sentiment Analysis in Python (Text Classification)
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.
Detection Projects
There are many “detection” projects you can do with Python. Instead of just naming one, I’m going to list by the level of difficulty those I implemented with Python.
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 (guides included): Scikit learn (TfidfVectorizer and PassiveAggressiveClassifier), Pandas and Numpy
- Source Code: 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
If you want to make this kind of project a bit more challenging, you can try 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 (guides included): 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 the chatbot with a dataset using data science techniques. As these chatbots process more interactions, their intelligence and accuracy will increase.
- Packages: Keras, NLTK, Numpy
- Source Code: How To Create A Chatbot with Python & Deep Learning In Less Than An Hour
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 customer. Information such as credit score, tenure, number of products, and estimated salary will be used to build this prediction model.
- Packages: Pandas, Matplolib, Scikit-learn, Machine Learning Algorithms (XGBoost, Random forest, KNN, Logistic regression, SVM, and Decision tree)
- Source Code: Bank Customer Churn Prediction
This project and the credit card fraud detection project are the most complete data science project listed in this article. It includes the exploratory data analysis, feature engineering, data preparation, model fitting, and model selection.
That’s it! Hope that after finishing all these projects, you understand much better everything you’ve learned about data science so far.
