Welcome to this collection of Python project ideas designed to help you build practical skills in Exploratory Data Analysis (EDA), Web Scraping, and Data Visualization. This repository is packed with hands-on project ideas that will guide you through real-world data manipulation and analysis tasks. Whether you're working with raw datasets, scraping data from websites, or building interactive visualizations, these projects will provide valuable insights into how data can be used to uncover trends, solve problems, and inform decisions.
- Web Scraping: Learn how to collect and analyze data from websites using Python tools like BeautifulSoup, Scrapy, and Selenium. Discover how to clean, extract, and store web data for further analysis.
- Exploratory Data Analysis (EDA): Gain hands-on experience with pandas, NumPy, and Matplotlib to explore datasets, clean data, and uncover patterns through visualizations.
- Data Visualization: Create interactive dashboards and visualizations using Matplotlib, Seaborn, and Plotly. Understand how to present data insights clearly and effectively.
- Data Modeling: Explore machine learning models to predict trends and forecast future outcomes based on your data.
This document serves as a central directory for a diverse collection of Python projects. The portfolio spans from fundamental data analysis and visualization to machine learning, data scraping, and automation scripts.
A project demonstrating advanced techniques in exploratory data analysis and data preprocessing.
An analysis of New York City Airbnb data to uncover trends and insights.
Data processing and predictive modeling using health data from Apple Watch and Fitbit devices.
Documentation outlining a task related to handling asynchronous dependencies.
A project focused on generating automated reports from data.
A dataset containing tweets related to a banking crisis, ready for sentiment analysis or topic modeling.
An exploratory data analysis of a marketing campaign to measure its effectiveness.
A collection of quick-reference cheat sheets for Python and its core data science libraries.
- Numpy Cheat Sheet (PDF)
- Pandas Cheat Sheet (PDF)
- Python Basics Cheat Sheet (PDF)
- General Python Cheat Sheet (PDF)
A project demonstrating classification and clustering machine learning techniques.
An analysis of store sales data using clustering methods.
Documentation for a project, detailing its approach and structure.
A dataset containing information about various companies.
An analysis project focused on computer company stocks.
Automation scripts for converting image files into other formats like strings (OCR) or PDFs.
A collection of web scraping tools, including scripts for IMDB and LinkedIn.
A collection of datasets for top-performing stocks across five key sectors.
- Automobiles Sector Data
- Banking Sector Data
- Energy Sector Data
- FMCG Sector Data
- IT Sector Data
- Pharmaceuticals Sector Data
A tool for scraping email addresses, with a specific implementation for Gmail.
An analysis of footballer Erling Haaland's performance data.
A collection of notebooks demonstrating EDA and hypothesis testing.
A guide to feature engineering techniques in Python.
A project demonstrating the calculation of Fibonacci numbers.
An in-depth analysis of the Forbes billionaires list using Seaborn for visualization.
A dataset for use in genomic prediction tasks.
A project to predict home loan approval using XGBoost.
- Loan Approval Classification Notebook (XGBoost)
- Trained Model File (HDF5)
- Training Data (CSV)
- Test Data (CSV)
A comprehensive exploratory data analysis of house price listings.
An analysis project focused on the population of India.
Project materials from an internship with Fittlyf.
Reference guides covering the fundamentals of the Python language.
- Reference Guide: Conditional Statements
- Reference Guide: Functions
- Reference Guide: Lists
- Reference Guide: Python Operators
An example of K-Means clustering applied to the Iris dataset.
A machine learning project to predict loan defaults.
A project using association rules to perform market basket analysis.
Resources and assessments for marketing analytics, focusing on Meta (Facebook).
- Measuring Facebook in Marketing Mix Models (PDF)
- Presentation Results Template (PPTX)
- Week 1 Assessment (PDF)
An analysis of video game ratings data from Metacritic.
A notebook designed to identify missing state information in a dataset.
A folder for a project analyzing India's Nifty 50 stock market index.
A dataset for classifying obesity levels.
A project that uses Optical Character Recognition (OCR) to read tables from documents.
Scripts for extracting data from PDF files and converting it into Excel format using the Tabula library.
A collection of various datasets for practice and exploration.
- Mock Data (CSV)
- Insurance Data (XLSX)
- Billionaires Data (CSV)
- Data Science Salaries (CSV)
- Football Boots Data (CSV)
- Graduation Rate Data (CSV)
- IMF Data Export (XLS)
Data and analysis related to the English Premier League.
- Player Stats (2022-23)
- Team & Match Results (2022-23)
An analysis project for the game Red Dead Redemption 2, including a Power BI file.
A sensitivity analysis performed for a mini-project, complete with context and data.
- Sensitivity Analysis Walkthrough
- Sensitivity Analysis Notebook
- Project Context
- Source Dataset Folder
A complete machine learning project for sentiment analysis, including preprocessing, feature extraction, training, and a server component.
- Project Overview (README)
- Main Execution Script
- Model Training Script
- Classification Script
- Flask Server Script
- Preprocessing Script
- Feature Extraction Script
- FastText Script
- Constants File
- Project Requirements
- Conda Environments
- Data Folder
Documentation, notebooks, and resources for a sentiment analysis project, covering supervised, unsupervised, and text normalization methods.
- Internship Project Documentation (PDF)
- Supervised Sentiment Analysis Notebook
- Unsupervised Lexical Sentiment Analysis Notebook
- Text Normalization Demo Notebook
- Text Normalizer Utility Script
- Contractions Utility Script
A dataset of Ulta skincare reviews for Natural Language Processing (NLP) tasks.
Projects related to scraping and analyzing Spotify data.
- Exploratory Data Analysis
- Spotify Scrapers
Notebooks demonstrating anomaly detection and data extraction from PDFs.
A Python script that programmatically generates stories.
A comprehensive dataset of top-performing mutual funds.
A project to analyze and predict traffic volume based on weather conditions.
An analysis of consumer spending trends in the UK over 25 years.
A historical dataset of tornado occurrences in the United States.
A dataset listing top universities for Computer Science.
An analysis project focused on the Red Wine Quality dataset.
Projects focused on scraping data from YouTube and performing exploratory analysis.
- Project 1: Data Exploration
- Project 2: Full Analysis
To complete these projects, you’ll need several powerful tools and libraries:
- Web Scraping: BeautifulSoup, Scrapy, Selenium, Requests
- Data Manipulation & Analysis: pandas, NumPy
- Data Visualization: Matplotlib, Seaborn, Plotly
- Machine Learning: Scikit-learn, TensorFlow, Keras (for predictive modeling)
- NLP: NLTK, SpaCy, TextBlob (for sentiment analysis and text classification)
- APIs: Tweepy (Twitter API), OpenWeatherMap (weather data), Yahoo Finance (stock data)