Day 32 at Luminar Technolab Focused on file handling in Python, reading data from files, processing it, and extracting useful insights. A small step closer to handling real world data. #Python #FileHandling #LearningJourney #ContinuousGrowth
Python File Handling and Insights
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🚀 Day 13 of My Python + DSA Journey Today’s problem focused on frequency counting 👇 ✅ Majority Element (#169) 💡 Majority Element Find the element that appears more than n/2 times 🔍 Approach: Used hashmap → counted frequency and returned element exceeding n/2 ⏱ O(n) time | O(n) space 🔥 What I learned today: • Hashmaps make counting problems simple • Frequency-based logic is very common in arrays • Early exit improves efficiency Getting faster at recognizing patterns ⚡ #Day13 #LeetCode #Python #DSA #CodingJourney #100DaysOfCode
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Ever struggled to find the right dataset? With the drawdata library in Python, you can sketch your own data and turn it into a dataset in seconds. In this example, I analyze it in R using k-means clustering, all within one Positron workflow. I just released a new module on this in the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #rstats #machinelearning #kmeans #statisticsglobehub
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Get started with machine learning using Python and discover how to build intelligent systems that can learn from data and improve their performance over time with this comprehensive guide https://lnkd.in/gDJ28K-Y #MachineLearningWithPython Read the full article https://lnkd.in/gDJ28K-Y
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🚀 Day 4/100 – Python Today I worked on solving a Quadratic Equation using Python. Instead of just writing code, I focused on understanding the logic behind it. Equation: ax² + bx + c = 0 Key learning from today: ✔ How to convert mathematical formulas into code ✔ Understanding the role of the discriminant (b² - 4ac) ✔ How different values of the discriminant affect the result ✔ Handling edge cases like negative values (using cmath) #100DaysOfCode #Python #ProblemSolving #LearningInPublic
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Day 2 of learning Pandas Today was all about cleaning data handled missing values, dropped unnecessary columns, and did some basic filtering. Starting to see how messy data becomes usable with the right steps #Python #Pandas #DataScience #LearningJourney
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🐍 Day2 of Python Learning Today, We explored: -What variables are and why they matter ✅ Numbers → int, float ✅ Text → str ✅ Boolean → True, False ✅ Using print() to display values It’s amazing how these simple concepts form the backbone of everything we build in Python. Every program starts with storing and manipulating data and today was a solid step toward that 💪 #Python #LearnPython #15DaysOfPython #Day2 #CodingJourney #Variables #DataTypes #PythonForBeginners #KeepLearning #GrowTogether
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Today, I learned how to take user input in Python using the input() function. This allows programs to interact with users and collect data such as name, age, and city. I also learned how to convert input into numbers using int() and float(), which is very important for calculations and data processing. #Day2 #Python #LearningJourney #DataScience #MachineLearning #Consistency
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Learn machine learning with Python and discover how to build and deploy AI-powered solutions with ease, with our comprehensive guide and tutorial https://lnkd.in/gTKhVnz5 #MachineLearningWithPython Read the full article https://lnkd.in/gTKhVnz5
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Learn how to use Python for machine learning with our ultimate guide, covering best practices, tips, and tricks for leveraging Python's power in machine learning applications https://lnkd.in/g2SeJ7nn #PythonForMachineLearning Read the full article https://lnkd.in/g2SeJ7nn
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Learn machine learning with Python and discover how to apply it to real-world problems with this comprehensive guide #MachineLearningWithPython Read the full article
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