Cross-Validation for Time Series Forecasting with TimeCopilot

🚀 Day 66 / 200 – Consistency is the real power 💡 Today’s challenge: **Implementing Pow(x, n)** using an optimized approach ⚡ Instead of the brute-force method, I used **Binary Exponentiation (Fast Power)** to reduce the time complexity from O(n) to O(log n). This approach efficiently handles both positive and negative powers, making the solution scalable and interview-ready. 🔹 Key Learnings: * Breaking problems into smaller subproblems improves efficiency * Handling edge cases (like negative exponents) is crucial * Optimization matters just as much as correctness 📊 Result: ✔️ 307 / 307 test cases passed ⚡ Runtime: 1 ms 🧠 Memory Efficiency: Top 2% Small improvements every day lead to big results over time. Staying consistent and sharpening problem-solving skills one challenge at a time 💪 #Day66 #100DaysOfCode #200DaysChallenge #LeetCode #Python #DSA #CodingJourney #ProblemSolving #Consistency

Built a Rainfall Prediction model and deployed it live. Here is what actually happened behind the scenes Decision Tree gave me 100% training accuracy. I got excited. Then I checked the test score and realised the model had just memorised the data. It learned nothing real. Naive Bayes gave me 73.9% on both train and test. Consistent That is the one I deployed. 3 models trained. 1 deployed. 1 lesson — a consistent score beats a perfect score every time live app here: https://lnkd.in/d-xaufug Full project and code: https://lnkd.in/d_d2Tx7R Akarsh Vyas Tanishq Vyas #DataScience #MachineLearning #Python #Streamlit #BuildInPublic #MLProject

🚀 Day 43 – LeetCode Journey Today’s problem: Minimum Depth of Binary Tree ✔️ Solved using recursion (DFS) ✔️ Carefully handled edge cases (null child nodes) ✔️ Calculated minimum depth correctly for uneven trees 💡 Key Insight: The minimum depth is the shortest path from the root node to a leaf node. We must handle cases where one subtree is missing — simply taking "min()" is not enough without checking null nodes. This problem improved my understanding of tree traversal and edge case handling in recursion 🌳 Learning to think deeper with trees every day 🔥💪 #LeetCode #Day43 #BinaryTree #DFS #Recursion #Python #ProblemSolving #CodingJourney #100DaysOfCode

Day 109 Backtracking patterns are repeating again — and that’s a good sign. #Day109 🧩 78. Subsets How today went: • Used recursion to explore all elements • At each step, decide to include or skip the current element • Append current subset → explore → then pop to backtrack • Move to the next index and repeat What I’m noticing: Subsets is one of the cleanest backtracking patterns: → choose → explore → undo Another revision day, but clarity is improving. Consistency continues. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

Day 106 Some problems feel simple when the pattern clicks. #Day106 🧩 17. Letter Combinations of a Phone Number How today went: • Used a digit → letters map • Built combinations using backtracking • Maintained a string path at each step • One recursive call per choice — no need for complex state handling What I realized: Once you understand the pattern: → choose a letter → move to next digit → build the path Backtracking becomes very natural. Simple problem, but great for building confidence. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

Day 111 Backtracking is starting to feel consistent now. #Day111 🧩 39. Combination Sum How today went: • Used backtracking with index i • Two choices: → stay at i (reuse the same element) → move to i + 1 (try next element) • Track the current total • If total == target → add result • If total > target → stop that path What I realized: This problem is about: → controlling index movement → managing the running total Same pattern, different control. Revision is making it clearer. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

🚀 I just made a small benchmarking toolkit called CodeQuantBenchmark. You can check it out here: https://lnkd.in/d6VUdHHK It’s a Python-based pipeline for automating benchmark workflows on quantized models applied to code-oriented tasks. The repo includes dataset preparation, optional fine-tuning, multi-precision quant conversions (e.g., various GGUF formats), and metrics covering quality (syntax validity, BLEU, Jaccard), latency (tokens/ms), and memory usage. CodeQuantBenchmark supports AST extraction, configurable experiments and reproducible reporting with built-in plots and test suites. 👉 Contributions (issues, enhancements, metrics, workflows) are welcome! #opensource #benchmarking #LLMs #quantization #python

Day 3 / 100 🚀 Solved “Reverse Integer” — a problem that looks simple but actually tests how carefully you handle edge cases. At first, reversing digits feels straightforward. But the real challenge is handling 32-bit overflow without using extra space. 💡 Key learning: Before updating the result, always check if multiplying by 10 will exceed the allowed range. Core idea: rev * 10 + digit must stay within [-2³¹, 2³¹ - 1] Highlights: • Time Complexity: O(log n) • Space Complexity: O(1) • Correctly handles negative numbers and overflow This problem reinforced a critical habit: Don’t just make the logic work — validate boundary conditions. #100DaysOfCode #LeetCode #DSA #Python #ProblemSolving #CodingInterview

While learning Conditional Operators and TRY / EXCEPT, I built this little program that honestly reflects real life more than I expected: The outcomes are brutally honest: ✅ Type 1 (Yes sunscreen) → Beach! 🏖️ 🧴 Type 2 (No sunscreen) → Library. No sunscreen, no fun. 📚 🤷 Type something random → Stay home, the program doesn't even argue. 🏠 💥 Type letters or symbols → Crash avoided, still staying home. 🏠 That's the real power of TRY / EXCEPT — instead of blowing up when something goes wrong, your code takes a deep breath and figures it out. No drama. Just solutions. 😂 #Python #DataScience #LearningInPublic #TryExcept #ConditionalOperators #CodingJourney #DataAnalytics

🚀 Day 85 of #100DaysOfLeetCode 🔍 Problem Solved: Ransom Note (LeetCode 383) Today’s problem was all about efficiently checking whether one string can be constructed from another — a classic hashing / frequency counting concept. ⚡ What I Learned: - Importance of frequency maps (hash tables) - Writing optimized solutions over naive approaches - How built-in methods can simplify logic but may impact performance 📊 Performance: ✅ Runtime: 0 ms (Beats 100%) ✅ Memory: Efficient usage 🔥 Takeaway: Small optimizations and choosing the right data structure can make a huge difference, even in easy problem #Day85 #LeetCode #CodingJourney #Python #DataStructures #ProblemSolving #100DaysOfCode