https://github.com/Madhuresh2011/Leetcode-Questions-Analysis-Using-Python

The LeetCode Question Analysis is a Python-based project designed to help users analyze and gain insights into LeetCode problems. It allows users to track, categorize, and evaluate their progress, identify patterns in problem-solving, and optimize the preparation.
https://github.com/Madhuresh2011/Leetcode-Questions-Analysis-Using-Python

analysis csv-files matplotlib-pyplot numpy pandas python python-project seaborn

Last synced: 5 months ago
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The LeetCode Question Analysis is a Python-based project designed to help users analyze and gain insights into LeetCode problems. It allows users to track, categorize, and evaluate their progress, identify patterns in problem-solving, and optimize the preparation.

• Host: GitHub
• URL: https://github.com/Madhuresh2011/Leetcode-Questions-Analysis-Using-Python
• Owner: Madhuresh2011
• Created: 2024-10-26T14:38:37.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-09T12:34:23.000Z (about 1 year ago)
• Last Synced: 2025-02-24T14:55:03.256Z (12 months ago)
• Topics: analysis, csv-files, matplotlib-pyplot, numpy, pandas, python, python-project, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 328 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Leetcode-Questions-Analysis

![leetcode analysis image](https://github.com/user-attachments/assets/fc402258-14ab-4753-aa27-e345ded6cf4f)

The LeetCode Question Analysis is a Python-based project designed to help users analyze and gain insights into LeetCode problems. It allows users to track, categorize, and evaluate their progress, identify patterns in problem-solving, and optimize the preparation.

### Data Extraction:

-- Automatically scrape or import problem data from LeetCode CSV export.

-- Include details such as problem title, difficulty level, tags, acceptance rate, and frequency.

### Visualization and Statistics:

-- Display problem-solving stats (e.g., total solved, difficulty distribution).

-- Visualize progress using bar charts, pie charts, or line graphs.

-- Highlight areas for improvement based on tag-based performance (e.g., Arrays, DP, Graphs).

### Problem Categorization:

-- Group problems by difficulty (Easy, Medium, Hard).

-- Filter unsolved or partially solved problems.

### Performance Tracking:

-- Track the average time spent per problem.

-- Analyze accuracy and revisit frequently failed problems.

-- Show historical progress over time.

### Search and Sort:

-- Implement search functionality to find problems by title, tags, or difficulty.

-- Sort problems based on acceptance rate, frequency, or user-defined criteria.

-- Export analysis data in formats such as CSV  for further processing.

### Python Libraries:

-- Data Handling: pandas, numpy

-- Visualization: matplotlib, seaborn, 

### Analysis:

-- Perform data cleaning and aggregation.

-- Generate visual and numerical insights.

### Conclusion:

-- The LeetCode Question Analysis Tool is a powerful Python-based project designed to streamline and enhance the problem-solving journey for coding enthusiasts and professionals.

-- By leveraging data analysis, visualization, categorization techniques and create meaningful insight from it.