As an aspiring data scientist, the best way for you to increase your skill level is by practicing. And what better way is there for practicing your technical skills than making projects. Personal projects are a really important part of your career’s growth. They will take you one step closer to your data science dream. Projects will boost your knowledge, skills, and confidence. Showcasing projects in your resume is going to make getting a data science job much easier.

“What projects should I make?” you ask? Well, do not worry for a second! With these amazing ideas for data science projects in 2020. So let’s start already!

Character recognition

This project focuses on the computer’s ability to recognize and understand the characters hand-written by humans. A convoluted neural network is trained using the MNIST dataset. This helps the neural network to recognize hand-written digits with reasonable accuracy. The project uses deep learning and requires the Keras and Tkinter libraries.

Driver drowsiness detection

Overnight driving is a tough job. A lot of accidents happen when a driver gets sleepy or drowsy while driving. This project aims to recognize when the driver might be falling asleep and raises an alarm.

This project uses a deep learning model to classify among images where people’s eyes are open or closed. It maintains a score based on how long the eyes remain closed. If the score increases further than a specified threshold. The model raises the alarm. To implement these projects make sure you are very well aware of all the basic concepts of Data Science.

Breast Cancer Detection

The breast cancer detection project uses histology images to classify whether the patient has Invasive Ductal Carcinoma or not. This project uses an IDC dataset to classify histology images as malignant or benign. A convoluted neural network is best suited for this task. The model is trained using about 80% of the dataset and the remaining dataset is used for testing the accuracy of the model after training it.

Impact of climate change on global food supply

Climate change and anomalies are becoming a common part of our world these days. This is starting to affect every aspect of human life on our planet.

This project focuses on quantifying the impact climate change is having and will have on global food production. The purpose of this project is to assess the potential impact of climate change on staple crop production. The project assesses the implications of temperature and precipitation change taking into account the effects of carbon dioxide on plant growth and the uncertainty in climate change. This project deals with data visualization and comparisons drawn between yields in different regions at different times.

Chatbot

Chatbots play an important role in businesses. They help in providing improved and personalized services and save manpower at the same time.

A chatbot can be trained using deep learning techniques. Using a dataset with a list of vocabulary, a list of common sentences, the intent behind them, and their appropriate responses. The most common methodology for training chatbots is to use Recurring Neural Networks (RNN). The bot consists of an encoder that updates its states according to the input sentence along with the intent and passes the state to the bot. The bot then uses the decoder to find an appropriate response. According to the words and the intent behind them. You can implement chatbot easily with Python.

Web traffic time series forecasting

Time series forecasting is a very important concept in statistics and machine learning. Predicting web traffic is a popular application of time series forecasting. It helps web servers to better manage their resources to avoid outages. To make the project even more interesting, you can use wavenets instead of traditional neural networks. Wavenets use causal convolutions which makes them more efficient and lightweight at the same time.

Fake news detection

The idea behind this project is to build a machine learning model that can detect whether the news given by any social media post is true or not. You can use the TfidfVectorizer, and a PassiveAggressive classifier to build this model.

TF or the Term Frequency is the number of times a word appears in a document.

IDF or the Inverse Document Frequency is a measure of the importance of a word based on the number of times it occurs in different documents. Common words that occur in many documents do not have high importance.

A TFIDFVectorizer analyzes a collection of documents and creates a TF-IDF matrix according to it.

A PassiveAggressive classifier remains passive if the classification outcome is correct but aggressively changes its classification criteria if the classification is incorrect.

Using these, we can build a machine learning model that can classify the news as fake or true.

Human Action Recognition

The human action recognition model looks at short videos of humans performing certain actions and tries to classify them based on what the action is. It uses a convoluted neural network trained on a dataset containing short videos and accelerometer data associated with them. The project first converts the accelerometer data into time-sliced representation. It then uses the Keras library to train, validate and test the network according to the dataset.

Forest fire prediction

Forest fires and wildfires have become alarmingly common disasters in today’s world. These disasters do a lot of damage to the ecosystem and also cost a lot in terms of money and infrastructure to deal with. Using k-means clustering, you can identify forest fire hotspots and the severity of a fire at that spot, which can be used for better resource allocation and faster response times. Using meteorological data like seasons during which fires are more common and weather conditions that exacerbate them can increase the accuracy of the results even further.

Gender and age detection

Gender and age detection is a computer vision and machine learning project. It uses convolutional neural networks or CNN. The project’s aim is to detect the gender and the age of a person by analyzing a single image of their face. The gender is classified as male or female and the age is classified among the ranges of 0-2, 4-6, 8- 2, 15-20, 25-32, 38-43, 48-53, 60-100. Due to factors like makeup, lighting, facial expressions, etc., recognizing gender and age form a single image can be difficult. Therefore, this project uses a classification model instead of regression.

Conclusion

With the knowledge of the right tools, there is no data science project that is too difficult. In fact, projects are the perfect way to improve your skills and progress towards their mastery.

These data science projects are the ones that will be very useful and trending in 2020. They will surely lead you to success. All you need to do is get started.