Day 39 of #60DaysOfMiniProjects Today I built a Placement Preparation System with GUI in Python Not just another project… This one actually helps you analyze your resume + prepare for interviews in one place. What this system does: • Upload your resume (PDF) • Extracts text and detects your skills • Compares with job-required skills • Calculates a match score (%) • Shows missing skills instantly • Generates interview questions (Python / DSA) • Clean and interactive GUI using Tkinter Why this is powerful: • Gives real insight into your placement readiness • Helps you focus on what you’re missing • Makes interview prep structured and smarter Concepts used: • File handling & PDF processing (PyPDF2) • Tkinter GUI development • Lists, dictionaries & string processing • Random module for dynamic questions • Basic scoring logic This project feels like a mini version of real hiring platforms Next improvements: • Add NLP for smarter skill detection • More roles & advanced questions • Timer-based interview simulation • Better UI/UX Building consistently. Learning daily. Improving step by step #Python #MiniProjects #BuildInPublic #CodingJourney #DeveloperLife #LearningInPublic #60DaysOfCode

Day 35 of #60DaysOfMiniProjects Today I built an interactive and smart Python project — an Interview Question Generator System Instead of randomly searching questions online, this system generates a structured interview set based on role and difficulty level — just like real interview prep platforms. What this project does: • Takes user input for role (Python/DSA) and difficulty level • Fetches relevant technical questions from a structured database • Randomly generates a balanced question set • Includes Technical + HR + Coding questions • Simulates a real interview preparation experience What it generates: • Curated technical questions based on level • Common HR interview questions • Coding practice problems • A complete mini mock interview set Concepts I worked with: • Dictionaries & nested data structures • Functions & modular programming • Random module for dynamic outputs • User input handling • Basic UI/UX in CLI (interactive experience) This project helped me understand how interview prep platforms can dynamically generate personalized question sets and improve consistency in practice. Next step: Adding timer + scoring system + GUI interface Learning step by step. Building consistently. Improving every day. #Python #MiniProjects #BuildInPublic #CodingJourney #DeveloperGrowth #LearningInPublic #60DaysOfCode

🚀 𝗚𝗿𝗼𝘂𝗽 𝗔𝗻𝗮𝗴𝗿𝗮𝗺𝘀 – 𝗙𝗿𝗼𝗺 𝗕𝗿𝘂𝘁𝗲 𝗙𝗼𝗿𝗰𝗲 𝘁𝗼 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 (𝗗𝗦𝗔 𝗝𝗼𝘂𝗿𝗻𝗲𝘆) Grouping anagrams is one of the most common interview problems — and a great way to understand hashmaps, strings, and optimization. Let’s break it down step by step 👇 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗱𝗲: https://lnkd.in/g5pP4F4j 🔴 𝗕𝗿𝘂𝘁𝗲 𝗙𝗼𝗿𝗰𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 👉 Compare each word with every other word 👉 Use character frequency to check anagrams 📌 Time Complexity: O(n² * k) 💡 Good for understanding logic, but not efficient 🟡 𝗕𝗲𝘁𝘁𝗲𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 (𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗖𝗼𝘂𝗻𝘁) 👉 Use a 26-length array (a–z) 👉 Convert it into a tuple as hashmap key 📌 Time Complexity: O(n * k) 💡 Faster and interview-friendly 🟢 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 (𝗦𝗼𝗿𝘁𝗶𝗻𝗴) 👉 Sort each word and use it as key Example: eat → aet tea → aet ate → aet 📌 Time Complexity: O(n * k log k) 💡 Clean, simple, and widely used ⚡ 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 ✅ Anagrams share the same sorted form ✅ Hashmaps are powerful for grouping ✅ Always think about time complexity ✅ Start with brute → then optimize 💬 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗶𝗽 If interviewer asks: 👉 “Can you optimize further?” → Use character count 👉 “Can you simplify?” → Use sorting approach 🔥 Practicing these patterns daily helps you crack coding interviews faster! #DSA #Python #CodingInterview #SoftwareEngineering #LeetCode #100DaysOfCode #Developers #Programming

🚀 Day 1 of building an Intelligent Resume Screening platform Today I focused on understanding the complete system flow before jumping into coding. Here’s what I learned: - How resumes are processed (PDF → text → analysis) - How job descriptions can be matched with resumes - How AI can be used to give suggestions 🧠 Key takeaway: Clarity before coding saves a lot of confusion later. Next step: Setting up backend using Python (Flask) #learning #coding #project #ai #placements

🚀 Python Interview Prep — But Make It Practical Most people memorize answers. Smart candidates understand why things work and when to use them. That’s what actually gets you hired. Here’s a structured way to prepare 👇 🔹 Core Python Foundations Instead of just knowing syntax, focus on concepts like: • is vs == (identity vs equality) • Efficient dict key checks • Writing clean list comprehensions • Handling duplicates smartly 👉 Interview tip: Always explain why your approach is optimal 🔹 Advanced Concepts (Where You Stand Out) • Memoization for performance • Generators vs Iterators (memory efficiency matters!) • Decorators (real-world use cases) • *args & **kwargs for flexible functions 👉 This is where interviewers test depth, not memorization 🔹 Data Handling (Must for Analytics Roles) • groupby, apply, transform • Writing clean pipelines with pipe • Filtering using query • Working with MultiIndex 👉 Tip: Show how you handle real datasets, not toy examples 🔹 NumPy Efficiency • Broadcasting vs loops • Vectorization for performance 👉 Key idea: Write code that scales 🔹 Visualization Skills • When to use Matplotlib vs Seaborn • Dual-axis charts for better storytelling 👉 Insight > charts. Always explain what the data is saying 🔹 Real-World Readiness • Custom exceptions + logging • Log parsing • Data cleaning strategies • Logical grouping (like login/session data) 👉 This is what separates job-ready candidates from beginners 💡 Final Thought: Interviews are not about knowing everything. They’re about showing how you think, structure, and solve problems. Are you preparing Python for interviews or real-world projects? 🤔 #Python #InterviewPreparation #Pandas #NumPy #PowerBI #SQL #DataAnalytics #DataEngineering #CareerGrowth

Most people prepare Python for interviews by memorizing answers. But interviews don’t test memory. They test how you think. 🧠 I recently went through a structured Python interview guide, and one thing stood out: It’s not about knowing what — It’s about knowing why and when. Nobody talks about this enough. The phase where: → You know syntax but struggle to explain concepts → You’ve seen generators, but can’t explain when to use them → You use pandas, but don’t know the difference between apply vs transform → You solve questions, but can’t justify your approach That’s where most candidates get filtered out. Here’s what actually matters: 🐍 1. Master Core Python deeply “is vs ==”, dict checks, list comprehensions — basics decide your foundation. ⚙️ 2. Understand advanced concepts clearly Generators, decorators, memoization — not just definitions, but real use-cases. 📊 3. Think in data workflows groupby, apply, transform, pipe — these aren’t functions, they’re data thinking tools. ⚡ 4. Optimize with NumPy Vectorization > loops. Always. Faster, cleaner, scalable. 📈 5. Know your visualization tools Seaborn for speed, Matplotlib for control — choose based on need. 🧩 6. Be ready for real-world scenarios Logging, exceptions, data cleaning, log parsing — this is where interviews become practical. The difference between average and selected candidates? 👉 They don’t just answer. They explain. If you’re preparing for Data Engineering, Analytics, or ML roles — this kind of preparation will save you hours. Want the full guide? Comment “Python Guide” and I’ll share it 📩 Follow for more coding resources, interview prep & career tips. 🚀 #Python #InterviewPrep #DataEngineering #Pandas #NumPy #Programming #TechCareers #GrowthMindset #IndianTechCommunity #LinkedInIndia

I JUST DID SOMETHING Just shipped your own personal resume parser and ATS scorer. The challenge? Reflect how your resume aligns to a job and see how your lacking. My approach: → Python + Flask for the web framework → PyPDF2 for text extraction → Beautiful Soup for scraping job description from job site → Modular pipeline: upload → parse → extract → structure → output JSON The best part? It handles messy real-world resumes, not just the perfect templates. Built this to understand how ATS systems work under the hood. Now I know why recruiters always say "keep it simple" 😅 Tech stack: Python, Flask, PyPDF2, Google-Genai, Langchain, ChromaDB #Python #NLP #WebDev #MachineLearning

💡 DSA Interview Prep Prompt! 💡 Are you preparing for Data Structures & Algorithms (DSA) interviews at top tech companies like Google? I’ve created a reusable DSA interview prep prompt to help you practice problem-solving and level up your coding skills! 💻🔥 What’s Inside: This DSA interview prep guide takes you step-by-step through the interview process, covering all critical aspects of solving coding problems: - Problem Understanding: Clarify the input/output formats, constraints, and edge cases. - Intuition & Approach: Understand the brute force vs optimal approach, and how to decide which to use. - Algorithm Design: Step-by-step explanation of the core idea, data structures, and flow of the solution. - Complexity Analysis: Time and space complexity breakdown, explaining why the solution is optimal. - Code Implementation: Clean, interview-ready Python code, handling edge cases. - Edge Cases: Identify and discuss common edge cases (empty input, large input, etc.). - Follow-Up: Be prepared to discuss optimizations, alternative approaches, tradeoffs, and scalability. Why This is a Great Exercise: - Perfect for DSA coding interviews at top companies like Google, Amazon, and Meta. - Helps you communicate effectively in interviews by explaining your thought process. - Focuses on key areas like efficiency, edge cases, and scalable solutions. Let’s Discuss! How would you approach solving coding problems in interviews? Share your approach below or feel free to DM me if you want to dive deeper into any of the concepts! 👇 Get the full guide here: https://lnkd.in/gYSK7sXy #DSA #TechInterviews #CodingInterview #InterviewPrep #SoftwareEngineering #Python #JobSearch #GoogleInterview

I built an AI app that roasts your resume 😭🔥 Meet Resume Assassin. Paste your CV or upload a file and it gives you: - a brutal roast - red flags - weak points - strengths - fixes - and a score Basically… your resume gets humbled in seconds 💀 Built with Python, Gradio, and Groq API. Would you try this? 👀 Test it out here: https://lnkd.in/dY6xAJ82 Ed Donner #BuildInPublic #PythonProject #AIApp #Developer #LLMEngineering #AI #ArtificialIntelligence #Gradio

Most array interview questions come down to just 5 techniques. 🧩 I've seen hundreds of developers struggle on LeetCode - not because they lack intelligence, but because no one taught them the patterns. Here are the 5 array techniques that show up in 80% of interviews: 1️⃣ HashMap / Frequency Count → Turn O(n²) brute force into O(n) with a dictionary → Use for: duplicates, anagrams, top-K elements 2️⃣ Two Pointers → Left & right pointers moving inward on a sorted array → Use for: pair sums, palindromes, reversing in-place 3️⃣ Sliding Window → Maintain a subarray and slide it across → Use for: max/min subarray of size k, longest subarray with condition 4️⃣ Prefix Sum → Precompute cumulative sums for O(1) range queries → Use for: multiple range sum queries, subarray sum = target 5️⃣ Binary Search → Halve the search space each step - O(log n) → Use for: sorted/rotated arrays and search on answer problems The secret? You don't need to memorize 500 LeetCode problems. You need to recognize which pattern fits. At AlgoNur, we teach these patterns with step-by-step animated visualizations so you actually SEE what's happening, not just memorize code. 🔗 Free. No login required. Try it at https://lnkd.in/dF-zfi4r Swipe through the carousel above to see each technique with code examples. ↑ ♻️ Repost if this helps someone you know preparing for interviews. #DSA #LeetCode #CodingInterview #DataStructures #Algorithms #SoftwareEngineering #Programming #AlgoNur