As we clearly know, artificial intelligence and data science have been the hottest trends in the past few years especially in 2019 and 2020. Such has been the trend and craze for these fields that it has surpassed the brightest of the careers like product management or software development, in terms of the number of searches and sentiment analysis on various social media platforms.
In such a scenario, there has been a large upsurge in the number of students wanting to get into data science and make a career in it. And it is not difficult to do it if you are determined enough to inculcate a particular skillset and keep striving for better and better. In this blog, we shall help you ease with your journey in data science by providing some really comprehensive data science projects in python. These shall let you have the relevant and necessary experience that you need to get into a data science or analytics role, and make your resume shine and stand out like anything.
But wait!!
Isn’t it important to first precisely understand the steps and the approach to be followed while working on a data science project?
Yes, it is!! In fact, the approach that you are following shall be the foundation for your project, and if the approach is not in place, the whole project shall go for a toss, regardless of how good the project idea is.
Before going ahead, it’s better to read this blog post on the essential skills for a data analyst/business analyst so that you get a clear understanding of the skillset you should possess to execute the projects and get started off in flying colors.
An ideal approach for a data science problem/project:
Generally, a problem statement should ideally be approached, through the following steps:
->Define problem statement/business problem.
->Collection of data.
->Data processing/cleaning
->Data analysis and exploration.
->Data modeling
->Deployment and optimization.
Lets understand about each of these steps in brief, before discussing the amazing bunch of data science project ideas.
Defining problem statement
First things first, define the problem you are going to solve, before getting into further details. Having a clear objective for the project will help you think and approach relevantly, without diverging much from the actual problem. This shall help you save time as well as resources considerably.
Collection of data:
Collect all the data needed to work on the project or solve that problem, collect as much data as possible if it seems relevant to you, how to use or not to use that data, that shall be taken care of in the forthcoming steps.
To be honest, collecting data may not be as easy as it may sound, the data is not going to be served on a platter on the web as well. You may have to research well and scrape out the data from sources like Kaggle or many more.
Data cleaning:
Data cleaning may honestly be regarded as a boring process in the world of data science, but trust me it is more important than any other process. Without cleaning the data, the insights that you may get may not be accurate and the whole process of analysis may go for a toss.
In essence, data cleaning is the process of removing the impractical, redundant, missing, and duplicate data from the datasets, this process may be time-consuming, but it has to be done anyway to get rid of the inconsistencies in the data.
Data analysis and exploration:
Once you are done cleaning the data, it’s time to observe and analyze the data. At this stage of the data science project cycle, you need to find meaningful patterns and trends in the data. This is the stage how you extract insights and useful information.
Data modeling:
At the end of the analysis stage, one must start framing hypothesis as to why a particular insight is the way it is.
Thereafter, before proceeding with building a model for our problem, data splicing is done. In this process, one proportion of data is kept to train the model, and the other is kept to test that model. This is followed by building the model using the training data set and testing that model with the test data set.
Deployment and optimization:
At this stage, one should try to improve the efficiency of the data model, which shall anyway help to enhance the quality of insights and the predictions being made through the model.
When the model is deployed, the users must validate the working and performance of the model, and generally, the errors are removed through this beta testing stage.
Now, I hope you’ve got a blueprint of the steps to be followed while working through a data science project!
Now let’s get to see the amazon data science projects in python, with source codes that shall put you much ahead of your competitors, be it for a job, internship or a hackathon, or anything for that matter!
Seems pretty fascinating, right! Trust me, this field shall become even more fascinating when you go ahead and start executing.
Have you still not started with data analytics or data science as a career option? Not to worry, read this blog on how you can start with data science/data analytics as a beginner
Data science projects in python for beginners:
–> Building chatbot
Chatbots for sure, play a vital role in scaling a business, given the fact that they resolve many of the common user queries effortlessly, and collect their problems, experiences, and feedback without any human involvement. This is done by deploying techniques backed by machine learning, and artificial intelligence.
They work by analyzing the customer input and reply appropriately through mapped responses. For training the chatbot, you can use recurrent neural networks, with the intents JSON dataset. On the other hand, implementation is being taken care of by python. The intelligence and accuracy of the chatbots keep increasing as they process more interactions.
Check out the source code and detailed tutorial on how to build your first Python chatbot.
–>Credit card Fraud detection
Credit cards have become more popular than ever lately, due to the effects of the pandemic, and consequently, credit card frauds have also skyrocketed like anything. Figuratively speaking, there have been over 16,000 credit card frauds happening on a daily basis, alone in India.
But by the courtesy of technologies like machine learning and artificial intelligence, credit card companies as well as the government have been able to combat and intercept these frauds to a large extent.
In a layman’s language, the idea beneath it is to analyze customer’s general spending behavior, including mapping the location of these spendings to differentiate the fraudulent transactions from the genuine ones, on a general level.
Python shall be the ideal language to use for this project, and it is one of those uncommon projects that can catch the reader’s attention easily.
To execute this project, one can use python with the customer’s transaction history(including locations) as the dataset and feed it to the decision trees, artificial neural network, and logistic regression. More data fed ultimately means greater accuracy.
Check the source code and a detailed tutorial for credit card fraud detection using python, and it shall become a cakewalk once you go through it entirely.
–> Driver’s drowsiness Detection
A number of road accidents take place on our roads on a daily level, and almost every time, the cause of the accident is the negligence of either of the drivers, in some form or the other. More often than nought, these accidents happen because of the drowsiness of the driver, which makes him lose control of the vehicle.
Hence, this is another social project, that has the potential to save thousands of lives every single day.
It does so by constantly assessing the driver’s eyes, and alerting him with alarms in case frequent closing of eyes is detected. A must-have for this project is a webcam, to allow the system to periodically monitor the driver’s eyes.
The execution of this project shall require framing a deep learning model and the libraries like OpenCV, Keras, and TensorFlow. Check out the detailed tutorial of driver’s drowsiness detection project with source code in this video.
–>Building recommendation systems
Have you ever given a thought that how do platforms like youtube, Netflix or Spotify show us the personalized content on their respective platforms, regarding what to watch/listen next.?
Wondered how do these platforms do that?
Well, as shown in the image above, it’s not any massive rocket science. It is done through a tool called a recommendation system. Several metrics are taken into consideration viz- genre preference recently watched shows, watch frequency, and even age. All this data is fed into a machine learning model which gives as output what the user may probably like to watch next.
Based on the input data, you can try to build a content-based recommendation system, which is relatively easier than other recommendation system tools.
Now this project can be done in any of two languages amongst Python and R, but the recommended language is python since it lets you use many more extensive libraries, which makes the recommendations even more accurate. You can use the MovieLens data set for this project, which has ratings for over 60,000 movies. The packages that shall be handy for this data science project are recommendorlab. Ggplot2, data.table and reshape.2 .
A detailed tutorial with the source shall also be easily available on web, just google it once 😉
–> Age prediction and gender detection
This project of age prediction and gender detection shall test both your machine learning skills as well as computer vision skills. We are required to build a system that takes in a person’s image as input and tries to identify their age and gender.
Again, python is the language to be used and Adience dataset can be used and given as input. OpenCV shall be useful package for this project.
Now, for this project, you can use implement Convolutional Neural Networks, and use the OpenCV package of python as mentioned. External factors like lighting, makeup, changing of facial expressions shall try to discard off your model, so these factors have to be kept in mind while testing.
Check out the detailed tutorial and source code for gender detection and age prediction project
–>Recognizing the speech emotions
One of the fundamental ways of expressing ourselves is through speech, and speech has various emotions hidden in it, namely anger, calmness, happiness, excitement etc. By analysis of the emotions hidden in the speech, it becomes possible to reframe our actions, services and products to offer an even more customised and personalised experience to certain clients.
This project hence extracts emotions from multiple sound files containing human speech and identify it.
To make this project in python, you may use the SoundFile, Numpy, Scikit-learn packages. For the dataset, the Ryerson audio-visual database of Emotional speech and songs database can be employed which has over 7200 files ready to use.
Again, you can check out this extensive tutorial with the source code for Recognizing the speech emotions project.
–>Fake news detection
Fake news basically refers to the false information or rumours spread through social media and other digital channels to fulfil some political motives.
It is one of the best data science project ideas, as it solves a social, practical problem, and this project is done properly and added to your resume shall make your resume shine like anything!
In this project, we shall build a model to accurately deduce that a piece of information is fake or real. A TfidfVectorizer and a PassiveAggressiveClassifier shall be built to classify the news into “real” and “fake”.
Some of the python libraries which may be handy for this project are Pandas, NumPy and Scikit-learn.
To know how this going to be executed, and for the source code, you may search on the web, and it shall easily be available, and you can check news.csv for the dataset.
The detailed tutorial with the source code for Detection of Fake news is here! Go through it, if this project seems fascinating to you.
–>Anomaly detection with PyoD
Anomaly detection is the process of recognizing and identifying unusual or impractical entries or patterns in the data.
Basically, it is defining a certain boundary around normal data entries in order to categorize the outliers specifically.
Let’s know a bit about PyoD, which is nothing but Python Outlier Detection. It is an extensive python toolkit for detecting outliers. It takes into account and implements more than 35 algorithms. It is developed with a comprehensive API in order to support multiple techniques.
For your knowledge, you can read more about the official documentation of PyoD.
And for execution of this project, here is a comprehensive tutorial to make you understand Outlier Detection in Python using PyoD library.
More data science project ideas using python for a strong portfolio:
Uber’s pickup analysis
Coronavirus visualisations
Detecting Parkinson’s disease
Earth Surface Temperature visualisation
and many more.
You may google them out, and you’ll surely get plenty of resources covering details about these projects.
Now, you have read so many project ideas for data science, would you like to read about what kind of questions can be asked in a data analyst and data scientist interview? Read this post for a curated set of data analyst interview questions and their ideal responses
Conclusion:
Through this article, I tried to cover more than 9 data science projects in python which shall be very handy to get you a high paying job or an internship. Being one of the hottest industries right now, data science and data analytics forsure are pretty promising and rewarding career options. If you are someone who is looking for a career switch, my first recommendation is getting into analytics and data science, and start learning a bit of programming(preferably in python) side by side. Programming knowledge may not seem be very useful in the beginning atleast, but if you have to take your data science journey to the next level, programming is a MUST without a second opinion.
So, to make the most out of the opportunities available in data science, you need to be super prepared to grab them as and when they knock. So, kickstart your journey with these awesome projects mentioned in this article, and I wish you all the very best with your journey and career in data science.