IPL Data Analysis

This project analyzes historical IPL (Indian Premier League) cricket match data using the Cricket_data.csv file. The analysis includes team performance, player statistics, most valuable players, and match outcomes. The Jupyter notebook (IPL_data_analysis.ipynb) includes step-by-step data cleaning, exploration. This project helps in understanding game dynamics and predicting future match outcomes.

Netflix Data Explorer

This project explores the global Netflix dataset provided in netflix_titles.csv. It examines trends in content release, country-wise production, content type distribution, ratings,Top Directors, Most frequent genre,content released per year,Find longest shortest title, All horrer movies,Group by country. The notebook (Netflix_data_explorer.ipynb) demonstrates data preprocessing, filtering, and visualization. Pandas and Matplotlib libraries are used to derive meaningful insights and patterns. This project is helpful for understanding what type of content Netflix offers globally.

Stock Market Analysis

This project analyzes Apple Inc.’s stock data to understand market trends and movements. Using the file 05_apple_stock_data.csv, it performs time-series analysis and calculates moving averages calculate volatility,moving averages,Daly returns The notebook (05_Stock_market.ipynb) visualizes stock prices, volume, and technical indicators. The project is useful for beginners interested in financial data analysis and investment strategies. It builds foundational skills in stock market data handling using Python.

Student Result Management

This project is focused on managing and analyzing students’ academic results. The dataset student_marks.csv contains information about student scores in different subjects. The notebook (Student_result_management.ipynb) calculates averages, grades, and top performers. It includes visualizations for better understanding of subject-wise performance. This tool can be extended for schools and colleges to track student progress.

Traffic Accident Analysis

This project uses traffic accident data to identify patterns and causes of road accidents. The dataset is analyzed in the notebook Traffice_accident_analysis.ipynb. It includes heatmaps, bar charts, and temporal analysis (month-wise/year-wise accident rates). This project shows how data science can be used to enhance public safety.

Weather Data Analysis

This project explores historical weather data from 04_weatherHistory.csv. The analysis includes temperature variation, humidity trends, wind patterns, and seasonality. The notebook 04_wether_data_analysis.ipynb is used for data cleaning, visualization, and insights. It helps understand climate behavior across time periods and locations. This project can be valuable for agriculture planning, weather forecasting, or climate research.