Data science projects are in high demand to create successfully among the community of data professionals that including data scientists. There is a wide range of data science projects with source code to brush up on the necessary data science skills or gain new hands-on experience on data science skills. Multiple tech companies as well as data-centric companies need to receive valuable portfolios consisting of meaningful data science projects with source code. Let's explore some of the top ten data science projects with source code to start working on in 2022.
Chatbot with Python is one of the top data science projects with source code and programming language, Python. It helps to train the chatbot with relevant information and analyze by leveraging complicated AI algorithms. It helps to offer data professionals both written as well as verbal responses.
Insurance claim severity is a popular data science project to brush up on the necessary data science skills for data professionals and data scientists. It helps to predict the type of claims an insurance company receives almost every day. This data science project with source code is implemented in Python with machine learning algorithms as well.
Sentiment analysis Data science projects can be completed by data professionals as well as data scientists with the integration of the R programming language. They need to brush up their data science skills with the help of a tiny text package for effective data analysis as well as offer scores for the corresponding words in the large dataset.
PUBG finish placement prediction is one of the leading data science projects for predicting how often online players will win the chicken dinner if they play by themselves. The data set is known for consisting of relevant data of players' statistics for 85,000 PUBG players. There are also 150 numerical game-play features per player to create this PUBG finish placement prediction.
Credit card fraud detection helps data professionals including data scientists to predict credit card fraud efficiently and effectively. Predictive models help to finish this data science project with transactional datasets. Data professionals can use logistic regression, support vector machines, k-means, random forests, and many more within a cross-validation framework.
Fake news detection is one of the top data science projects with three different modules to be used for completing this project— NumPy, Pandas, and Sklearn. The dataset should have four columns as the first one to identify the news, the second one for the title, the third one for text, and the fourth one is the label— fake or real. It helps to enhance the practical data science skills with necessary imports and splitting the dataset.
Recommender system is another one of the favourite data science projects for data professionals including data scientists. One can opt for three types of recommender systems such as content-based filtering, collaborative filtering, and hybrid recommendation system. The process includes checking the head of the data, checking out all the movies with respective IDs, calculating the mean rating of all kinds of movies, calculating the count rating of all movies, creating a data frame with rating count values, and many more steps.
House price prediction offers to enhance data science skills for data professionals including data scientists. One should load home prices into a data frame while dropping multiple features that are not necessary for building the data science project and also some unnecessary columns. One should add new features for BHK, explore the total sq. ft. feature, and price per square, as well as examine locations as a categorical variable.
Wine quality prediction is one of the top data science projects with the R programming language. The main purpose of the wine quality project is to explore the types of chemical properties that influence the quality level of red wines. One can also brush up on the concepts regarding data science including exploratory data analysis, components of a dataset, data munging, and many more.
Parkinson's disease detection data science project needs to use one of the classifier techniques for its output with only 1s and 0s. Data professionals and data scientists can load the datasets, receive the features and targets, divide those into training and testing sets, as well as finally pass them to RandomForestClassifier for the prediction of Parkinson's disease. One should import libraries, preprocess, normalize, train and test, and build the classifier model.
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