It can be intimidating to begin a data science journey, but doable tasks are a great way to put the theories you’ve studied into practice and hone the necessary skills. The top 9 data science projects for novices are listed below; these projects will help you practice your abilities and learn more about the field of data science by incorporating analysis, visualization, and even machine learning.
Public Dataset Exploratory Data Analysis (EDA) Goal:
The candidate should choose a publicly available dataset for this challenge from the University of California, Irvine Machine Learning Repository (UC-MLR) or the Kaggle toolbox.
Only the preprocess and clean-up data will need to be finished in this step.
Instruments:
Jupyter Notebook
Sentiment analysis of Twitter data from social media platforms
Determine what proportion of tweets are neutral, negative, and positive.
Actions:
It is crucial to note that the tweets should be gathered with the assistance of the Twitter API as a continuation of the preceding stage.
Additionally, it’s critical to clean up the data using tokenization and get rid of terms like stop words that don’t accurately convey the context.
Using the proper set of techniques, develop a sentiment analysis machine learning model.
Instruments:
Text (Natural Language Toolkit) on Twitter (Tweepy) Modeling Predictively (Scikit-learn)
Jupyter Notebook
Predictive Modeling Using Data on Housing Prices
Describe a project that would entail creating a model that could determine a house’s price based on specific characteristics.
Actions:
Choose a dataset for analysis that is comparable to the Boston Housing Dataset, which was utilized in the research that follows.
Investigating the dataset and configuring features.
Regression models should be fine-tuned, and their level of generalization should be determined.
Instruments:
Jupyter Notebook
Classifying images with the MNIST dataset
Moreover, handwritten digit images can be classified using machine learning methods as Naive Bayes, K-Nearest Neighbors, Support Vector Machines, and others.
Actions:
The training set should first be loaded from the MNIST data and lightly preprocessed.
Tsang, Victoria (2017). Ideally, we should assess the model’s correctness and adjust the hyperparameters after fitting. The Whole [k-nearest neighbors] Guidebook.
Instruments:
Python is a programming language. Machine learning frameworks: Pytorch, Keras, TensorFlow
Jupyter Notebook
Utilizing Clustering to Segment Customers
Customer segmentation, which groups customers based on how they make purchases, is an effective way to manage customer relationships.
Actions:
Choose from among the retail data sets.
Data preparation and exploration through exploratory data analysis and feature selection are the first steps in every data mining project. These steps are as follows:
Instruments:
Scikit-learn for machine learning, Matplotlib for data visualization, and Pandas for data manipulation
Jupyter Notebook
Forecasting Time Series Using Stock Prices
Make future stock price predictions based on historical data collected from the market.
Actions:
Compiling information on past stock price quotations and other stock market data is an additional approach.
Create the discrete time models by beginning with an LSTM or ARIMA model.
Instruments:
Jupyter Notebook
A Movie Recommendation System
Make suggestions in order to create a system that will recognize movies that are appropriate for consumers’ tastes.
Actions:
In particular, the following guidelines should be adhered to while utilizing the MovieLens dataset or any other database that is comparable:
Utilize content-based and collaborative filtering techniques.
Instruments:
Jupyter Notebook
Text classification using natural language processing
In other words, it entails grouping text documents into a collection of compiled groups, or classes.
Actions:
Choose a text corpus (news articles on related subjects in your field of interest, for example).
Clean up the data and convert the text into vector form for use as features.
This entails training and assessing a model for text classification.
Instruments:
Jupyter Notebook
Goal of Anomaly Detection in Network Traffic
Finding behavioral patterns or data flow patterns that deviate greatly from average levels sets this apart.
Actions:
You can use the program’s net flow data set.
Wash the data and run extra processes on datasets to achieve this. Preparing the data and investigating it.
Instruments:
Python with additional Matplotlib, Scikit-learn, and Pandas features
Jupyter Notebook
The titles of the topics being presented include data analysis and data visualization, machine learning, and natural language processing, among others. These hands-on projects increase learners’ understanding of data science. Even though the project solutions offer a higher degree of complexity, it will still be helpful for novices because it will help them build the fundamental knowledge of data science that they will need to be able to tackle more complex problems in the future.
