--> Movie Rating Analysis

This repository contains a Python-based movie rating analysis project. The project utilizes datasets containing movie titles, genres, and user ratings to perform various analyses, including the distribution of ratings and identifying top-rated movies.

Features

• Data Merging: Combines movie and rating datasets for comprehensive analysis.
• Rating Distribution Visualization: Creates a pie chart to visualize the distribution of movie ratings.
• Top Rated Movies: Identifies the top 10 most frequently rated movies with a perfect score of 10.

--> 🎢 Roller Coaster Data Analysis and Visualization

This project analyzes and visualizes data from various roller coasters worldwide, exploring their characteristics such as speed, height, and introduction year. Using Python, the dataset is cleaned, prepared, and explored to reveal key trends in roller coaster design and performance.

Features

• Data Cleaning: Irrelevant columns removed, duplicates handled, and data types standardized.
• Data Visualizations:
  1. Top 10 Years for Coaster Introductions: Bar chart of the most frequent coaster introduction years.
  2. Coaster Speed Distribution: Histogram showing frequency of different coaster speeds.
  3. Speed vs Height: Scatter plot revealing the relationship between speed and height of coasters.

--> Audio Processing and Analysis with Python

This project demonstrates how to process and visualize audio files using Python libraries like librosa, pandas, and matplotlib. It covers essential audio concepts and techniques such as waveform plotting, trimming, and creating spectrograms and mel spectrograms.

Features

• Load and visualize audio waveforms
• Trim silence from audio files
• Generate spectrograms and mel spectrograms
• Zoom into specific sections of audio data

--> Supply Chain Data Analysis and Visualization

This project analyzes a supply chain dataset using Python libraries like Pandas and Plotly to visualize key metrics such as sales, revenue, manufacturing costs, and defect rates. The goal is to provide actionable insights that can help improve operational efficiency and decision-making in supply chain management.

Key Features

• Price vs. Revenue Analysis: Scatter plot showing the relationship between price and revenue by product type.
• Sales Distribution: Pie chart representing the sales distribution by product type.
• Shipping Costs & Revenue: Bar charts to analyze revenue and shipping costs by different carriers.
• Lead Times & Manufacturing Costs: Analysis of average lead time and manufacturing costs by product type.
• Defect Rates: Bar chart showcasing average defect rates by product and transportation mode.