Welcome to my comprehensive collection of Data Science and Machine Learning projects. This repository showcases a variety of projects, each demonstrating the application of different techniques and tools to solve real-world problems. These projects cover areas such as stock market analysis, predictive modeling, data visualization, and web scraping, reflecting a robust understanding of machine learning and Python programming.
Objective: Implement a classic Rock-Paper-Scissors game where users can play against the computer.
Tools/Libraries Used: Python, random module.
Key Insights: Demonstrated the ability to use Python for simple game development and random choice generation to simulate computer moves.
Objective: Develop a program to count the frequency of words in a given string and determine the length of the highest-frequency word.
Tools/Libraries Used: Python, re module (Regular Expressions).
Key Insights: Utilized regular expressions to process text data efficiently and demonstrated basic text analysis.
Objective: Create a program that generates the multiplication table for any given number.
Tools/Libraries Used: Python.
Key Insights: Showcased the ability to handle user inputs and perform repetitive calculations using loops.
Objective: Check if all characters in a string appear the same number of times or if removing one character can achieve this.
Tools/Libraries Used: Python, Counter from collections module.
Key Insights: Demonstrated logical thinking and the ability to use Python's collection tools for frequency analysis.
Objective: Allow users to create a dictionary of personal details for a group of people, including name, age, and occupation.
Tools/Libraries Used: Python.
Key Insights: Demonstrated proficiency in handling dictionaries and user inputs to create structured data.
Objective: Compare the stock prices of tech giants (Apple, Google, Microsoft, Amazon) to analyze performance and trends.
Tools/Libraries Used: Python, pandas, pandas_datareader, datetime.
Key Insights: Implemented data retrieval, analysis, and visualization to identify the best-performing stock over a specified period. Showcased trend analysis and percentage change calculations.
Objective: Analyze FIFA 19 player data to uncover how player attributes affect overall ratings and predict ratings using regression analysis.
Tools/Libraries Used: Python, pandas, numpy, matplotlib, seaborn, sklearn.
Key Insights: Conducted extensive data cleaning, correlation analysis, and linear regression to determine key attributes influencing player ratings. Highlighted top players and their defining attributes.
Objective: Analyze shipment data to uncover pricing trends and detect anomalies.
Tools/Libraries Used: Python, pandas, seaborn, matplotlib.
Key Insights: Identified outliers and provided descriptive statistics to highlight pricing trends within the supply chain. Emphasized the importance of data completeness and quality.
Objective: Visualize various aspects of the Titanic dataset to uncover insights about passengers and their attributes.
Tools/Libraries Used: Python, pandas, seaborn, matplotlib, wordcloud, numpy.
Key Insights: Generated multiple visualizations including bar graphs, scatter plots, pie charts, histograms, and heatmaps to analyze demographics, fare distributions, and survival rates.
Objective: Write a program to scrape various types of data from web pages, including all text, specific div elements, and tables.
Tools/Libraries Used: Python, requests, beautifulsoup4.
Key Insights: Demonstrated the ability to extract structured and unstructured data from web pages, showcasing versatility in data collection techniques.
This repository highlights my ability to apply Python and machine learning techniques to solve diverse problems. Each project reflects a strong foundation in data manipulation, analysis, and visualization, making this portfolio a testament to my skills and dedication. I am confident that these projects will make a significant impact and demonstrate my readiness for a role in data science and machine learning.
Feel free to explore each project and reach out if you have any questions or feedback!