Top Data Science Projects in Python

Top Data Science Projects in Python

Top data science projects in Python to help you realize your potential in 2024

Data science is a multidisciplinary field that involves extracting insights from data using various methods and tools. Python is a popular programming language for data science, as it offers a rich set of libraries and frameworks for data analysis, visualization, and machine learning. Here are some of the top 10 data science projects in Python with details:

1. Music Recommendation System on KKBox Dataset

The goal of this data science project is to create a music recommendation system utilizing the KKBox dataset, which comprises data on the songs, artists, users, and listening habits of the biggest streaming platform in Asia. The goal of the project is to forecast the probability that a user would listen to music frequently using machine learning models, feature engineering, and data research.

2. Age of Abalone Shells Data Analysis

The objective of this research is to use the Abalone dataset, which includes physical measures of the shells including length, diameter, height, weight, and rings, to assess the age of abalone shells. Regression models, descriptive statistics, and data visualization are used in this study to determine the age of the shells based on their rings.

3. Premier League Data Analysis

The purpose of this data science project is to investigate, evaluate, and present the events from the English Premier League, the highest division of the English football league system, in 2018–2019. Using the Soccer Data dataset which offers comprehensive details on the games, teams, players, and events the project entails data processing, aggregation, and charting.

4. Stock Market Analysis

The goal of this project is to use the Stock Market dataset, which includes daily prices and volume information for a variety of equities from 2010 to 2017, to do a thorough analysis of the stock market. The project includes time series analysis, correlation analysis, clustering analysis, and data cleansing, transformation, and visualization.

5. Netflix Recommendation System

Building a recommendation system for Netflix, the top streaming service in the world, is the goal of this project. Using the Netflix Prize dataset, which comprises user ratings for over 17,000 films from over 480,000 people, the research entails data pretreatment, exploratory data analysis, and collaborative filtering.

6. House Rent Prediction

The goal of this research is to use the House Rent dataset, which includes details on over 21,000 houses' locations, sizes, amenities, and rent, to forecast the rent of houses in various cities. Data cleansing, feature engineering, and machine learning models like random forests, decision trees, and linear regression are all part of the project.

7. Password Strength Checker with Machine Learning

This project uses machine learning methods, including categorization and natural language processing, to create a password-strength tester. Employing the Password Strength dataset which comprises more than 6,000 passwords and their strength labels the project entails data collection, preprocessing, vectorization, and model training, assessment, and deployment.

8. Classification Model Evaluation

The goal of this research is to assess how well various categorization models perform using a range of measures, including roc curve, accuracy, precision, recall, f1-score, and confusion matrix. Using the Breast Cancer dataset, which comprises the characteristics and diagnoses of 569 individuals, the project entails data loading, splitting, and scaling in addition to model construction, testing, and comparison.

9. Credit Card Fraud Detection as a Classification Problem

To identify fraudulent credit card transactions, this project makes use of machine learning models including k-nearest neighbors, logistic regression, and support vector machines. Using the Credit Card Fraud Detection dataset which comprises the transactions and labels of over 280,000 credit cards the project entails data exploration, feature selection, and resampling in addition to model training, validation, and tuning.

10. Predict Quora Question Pairs Meaning using NLP in Python

The goal of this project is to use natural language processing techniques, such as text preprocessing, word embedding, and deep learning, to predict if two Quora questions imply the same thing. In addition to creating, training, and evaluating a model utilizing the Quora Question Pairs dataset which has over 400,000 question pairs and their corresponding similarity labels the project also entails loading, cleaning, and separating data.

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