Pandas Basics and Data Analysis with Luminar Technolab

Day 39 at Luminar Technolab Focused on data manipulation in Pandas adding columns, sorting values, and filtering using conditions. Also explored useful methods like head() and tail() for quick data insights. #Python #Pandas #DataAnalysis #LearningJourney #Consistency

Day 47 at Luminar Technolab Handled missing data in the Diabetes dataset using: • Drop rows (dropna()) • Impute with random values (fillna()) • Mean, median, and mode for imputation Focusing on how to clean data for analysis. #Python #Pandas #DataCleaning #MissingData #LearningJourney

Day 41 at Luminar Technolab Continued working with datasets using Pandas filtering data, selecting specific columns, and analyzing segments. Used functions like value_counts() to identify most and least frequent categories, and sorted data for better insights. Getting more comfortable asking questions to data. #Python #Pandas #EDA #DataAnalysis #LearningJourney

Day 40 at Luminar Technolab Worked with a real dataset using Pandas loading data from CSV and exploring it. Performed basic EDA steps like checking null values, data types, unique values, and dataset shape. Getting a clearer picture of how real-world data is analyzed. #Python #Pandas #EDA #DataAnalysis #LearningJourney

Day 38 at Luminar Technolab Worked with Pandas DataFrame selection using iloc and explored different ways to access rows and columns. Getting more comfortable navigating structured data #Python #Pandas #DataAnalysis #LearningJourney

🚀 Matrix Multiplication: Code Implementation (Data Structures And Algorithms) This Python code illustrates how to perform matrix multiplication. The function takes two matrices as input and returns their product. It ensures that the matrices are compatible for multiplication (number of columns in the first matrix equals the number of rows in the second). The algorithm iterates through the rows of the first matrix and the columns of the second matrix to compute each element of the resulting matrix. Understanding the nested loops and the dot product calculation is key to understanding matrix multiplication. #Algorithms #DataStructures #CodingInterview #ProblemSolving #professional #career #development

Task 3(Intermediate Level): Clustering Analysis (K Means) Description: Implement K-Means clustering to group similar data points together based on feature similarities. Tools: Python, scikit-learn, matplotlib, seaborn I standardized the dataset (using StandardScaler). I applied K-Means clustering and determined the optimal number of clusters using the elbow method. I visualized the clusters using 2D scatter plots. #CodvedaAchievements #CodvedaProjects #CodvedaJourney #CodvedaExperience #FutureWithCodveda Codveda Technologies

Day 24/100 – DSA Journey Problem: Intersection of Two Arrays Initially thought of using loops, but that would increase time complexity. Then realized the problem requires only unique common elements, which makes sets a perfect fit. Converted both arrays into sets and used intersection to directly get the result in an efficient way. Key Learning: Choosing the right data structure can simplify the entire problem. #Day24 #100DaysOfCode #DSA #Python #LeetCode #ProblemSolving

DAY 53 🚀 – Bit Manipulation + Subsets 🔥 Exploring power sets and pattern expansion 💯 Day 53 / #120DaysOfCode – LeetCode Challenge ✅ Problem Solved: • 78. Subsets 💻 Language: Python 📚 Key Learnings: • Built subsets using iterative expansion technique • Understood relation to power set (2ⁿ combinations) • Learned alternative approach via bit manipulation • Strengthened understanding of combinatorics in coding 💡 Key Insight: Each element doubles the number of subsets → include / exclude 🔥 Progress: Moving deeper into patterns used in recursion & bit manipulation 💪 🔗 LeetCode Profile: https://lnkd.in/gbeMKcv5 #LeetCode #Python #DSA #BitManipulation #Subsets #Backtracking #Consistency #120DaysOfCode

Day 36 at Luminar Technolab Worked with NumPy operations on arrays max,min,argmax,argmin (overall, row-wise, and column-wise). Also explored matrix operations and creating structured arrays using zeros and zeros_like. Moving closer to real data analysis patterns #Python #NumPy #DataAnalysis #LearningJourney #Consistency