Data Cleaning & Preprocessing: Demonstrate how you handle messy data, deal with missing values, outliers, and normalization. Data: https://www.kaggle.com/c/titanic/data Exploratory Data Analysis (EDA): Show how you explore data to uncover patterns, trends, and insights with visualizations and statistics. Predictive Modeling: Create a repository for building and validating models using machine learning algorithms. Performance Optimization: Highlight how you optimize your models or scripts for speed and efficiency.
Structure Repositories:
Problem Statement: Describe the problem you're solving (e.g., data leak analysis, anomaly detection). Dataset: Provide a link to the dataset (or mock data if proprietary). Analysis/Approach: Describe your steps, tools, and methods (e.g., Python, Pandas, NumPy, SQL, etc.). Results/Insights: Share visualizations, insights, and findings. Challenges & Learnings: Mention any obstacles and how you overcame them.