Python Projects with Source Code – Practice Top Projects in Python
Python Projects with Source Code
Looking to build a career in Python? Want to improve your resume with multiple personal projects on it? Then this blog of Python projects with source code is for you. You earlier read about the top 5 data science projects; now, we bring you 12 projects implementing data science with Python. In this blog, you’ll find the entire code to all the projects. Read on to give your data science/ Python career a head-start.
List of amazing Python Projects with source code:
- Tic Tac Toe project
- Fake News Detection project
- Parkinson’s Disease Detection project
- Color Detection project
- Speech Emotion Recognition project
- Breast Cancer Classification project
- Age and Gender Detection project
- Chatbot project
- Driver Drowsiness Detection project
- Traffic Signs Recognition project
- Image Caption Generator project
- News Aggregator App project
- Handwritten Digit Recognition project
Why do Projects in Python?
In an interview, a resume with projects shows interest and sincerity. Spending time on personal projects ultimately proves helpful for your career. In this blog of Python projects, we try our best to include different data science and machine learning libraries of Python to give you a better experience.
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Top Python Projects with Source Code
Let’s start discussing python projects with source code:
1. Detecting Fake News with Python
Fake news can be dangerous. This is a type of yellow journalism and spreads fake information as ‘news’ using social media and other online media. This is a common way to achieve a certain political agenda. Fake news may contain false and/or exaggerated claims. Social media algorithms often viralize these and create a filter bubble. In this, we will train on a news.csv dataset of shape 7796×4. We’ll mainly use two things- a TfidfVectorizer and a PassiveAggressiveClassifier. A TfidfVectorizer turns a collection of raw documents into a matrix of TF-IDF features. And a PassiveAggressiveClassifier is an online learning algorithm that stays passive for a correct classification and becomes aggressive when there’s a miscalculation.
Please refer – Detecting Fake News with Python for the complete implementation of the python project with source code.
2. Detecting Parkinson’s Disease with XGBoost
Parkinson’s disease is a progressive disorder of the central nervous system that affects over 1 million people in India every year. It affects movement and can be a cause of tremors and stiffness. This is a neurodegenerative disorder with 5 stages to it, and affects dopamine-producing neurons in the brain. In this python project, we will use the UCI ML Parkinsons dataset and use XGBClassifier from xgboost to build a model that can accurately detect the presence of Parkinson’s disease in a person. We will also use the libraries scikit-learn, numpy, and pandas.
Check the complete python project with source code – Detecting Parkinson’s Disease with Python
3. Color Detection with OpenCV and Pandas
As we all know that colors are made up of three primary colors: Red, Green, and Blue. Their intensities can be measured between 0 to 255 and by combining them we get 6 million different color values. This idea of this project is to get the name of the color from the color values. To implement this we use a dataset that has color values and labeled colour names, then we calculate the shortest distance between each colour and display the colour name that has the shortest distance.
Work on the interesting Python Project on Color Detection now!!
4. Speech Emotion Recognition with librosa
Speech Emotion Recognition (SER) is an attractive application of data science today as we constantly attempt to give the consumer a better experience. This includes recognizing human emotion and affective states from speech. Since voice often exposes underlying emotions with tone and pitch, it can be used to understand the users’ needs and use it to improve the service. We will use the RAVDESS dataset and the libraries librosa, soundfile, and sklearn to build a model using an MLPClassifier. In most projects, we use Jupyter Lab to run our code.
Ready to build your own model? Speech Emotion Recognition Python Project with Source Code
5. Breast Cancer Classification with Keras
IDC (Invasive Ductal Carcinoma) is the most common form of breast cancer, forming about 80% of all breast cancer diagnoses. This is a cancer that develops in milk ducts, and then invades the fibrous/fatty breast tissue outside them. In this project, we use the IDC_regular dataset (with breast cancer histology images). Histology is the study of the microscopic structure of tissues. With 2,77,524 patches of size 50×50 from 162 whole mount slide images scanned at 40x, we’ll learn to build a classifier to train on 80% of the dataset. We’ll use 10% of it for validation. We’ll be using Keras to define a CNN (CancerNet).
Excited? Check the entire python project of breast cancer classification with source code
6. Gender and Age Detection with OpenCV
Computer Vision is a field of study enabling computers to see and recognize digital images and videos- this is something only humans (and animals) are generally capable of. This involves processes like object recognition, video tracking, motion estimation, and image restoration. It is exciting to be able to predict a person’s gender and age from just a photograph. CNNs (Convolutional Neural Networks) are often the choice when we work with images. In this project, we’ll use OpenCV (Open Source Computer Vision) and implement deep learning, using trained models on the Adience dataset.
This is just a brief, explore detailed Gender and Age Detection Python Project with source code
7. Chatbot with NLTK and Keras
I’m sure you have talked with machines several times. Be it your google assistant, Alexa, Siri or some intelligent bot on a website. A chatbot can even take your pizza delivery orders. Aren’t you curious about how this work? How a machine can understand what is being said and give appropriate responses. All of this involves an understanding of natural language processing which consists of many concepts that are involved to make a machine understand a human language like English. We have implemented a retrieval based chatbot using deep learning techniques. The technologies used in the project are Python, NLTK library and Keras library.
Curious to work? Check the complete Python Project on Chatbot
8. Driver Drowsiness Detection with OpenCV and Keras
Driving while the driver feels drowsy is extremely dangerous and thousands of accidents happen each year because the driver fell asleep. The drowsiness detection system can save a life by alerting the driver when he/she feels drowsy.
This python project is implemented using OpenCV and Keras. With OpenCV, we are detecting the face and eyes of the driver and then we use a model that can predict the state of a person’s eye “Open” or “Close”. The classification of eyes is done by a Convolutional Neural Network (CNN) model which is a deep neural network we build in Keras. CNN works extremely well for image classification purposes and this is why our model is based on CNN.
The project looks interesting. Isn’t it? Check the complete implementation of the Intermediate Python Project on Drowsiness Detection System with Source Code
9. Traffic Signs Recognition with Keras and CNN
We are moving towards the future where we will travel in driverless cars. The cars will be fully automated and you will just have to pick your destination and the car will take you there. A very important challenge of autonomous vehicles is how to follow the traffic signs on the road. The traffic sign recognition is the act of automatically identifying a traffic sign from an image. The project is implemented by building a deep neural network model using the Keras framework. The model is capable of identifying 43 different types of traffic signs. You will also learn to build a GUI for easily interacting with the application.
Work on the Traffic Signs Recognition Python Project with complete implementation and source code
10. Image Caption Generator with CNN & LSTM
The aim of the project is to build a model that will automatically generate captions of an image. Humans can easily understand an image by looking at them but this is a hard task for computers. It uses image processing concepts and natural language processing to build the image caption generator model. The model can be used to automatically generate captions for stock images websites, it can also become a hearing aid for blind people. This has many applications and a very good project to understand deep learning concepts and perform natural language processing.
Want to work on the project? Check the complete implementation of Python project on image caption generator with source code
11. News Aggregator App with Django Framework
How much time do you spend checking on different news websites and check out the latest news content? On the internet, we don’t rely on a single website to check all the content so we visit multiple websites which is time-consuming. The goal of a news aggregator is to reduce this time and effort by combining all the different websites into a single web application. You can then check the latest news from all the different websites from one place. In this project, we are going to build a News Aggregator web application using the Django framework, we will be using the requests library to make HTTP requests and fetch the content using BeautifulSoup library.
Build your own News Aggregator App with Python Django Project and enhance your skills
12. Handwritten Digit Recognition with Deep Learning
Machines are getting more and more intelligent nowadays. With new machine learning and deep learning techniques, it is exciting to see what developers can build. The handwritten digit recognition project is an excellent project to explore the machine learning field. In this project, you will use the MNIST dataset to build a model that can recognize the handwritten digits using convolutional neural networks.
Work on the Handwritten Digit Recognition Python Project with Source Code
We have learned to build 12 exciting Python projects with source code. Try them at your own end and pay attention to every step as you do it. Which one was your favorite? Tell us in the comment section.
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