ANMOL SAHU’s Post

I expected Random Forest to win. It didn't. Built a sales forecasting model on ecommerce data. Tried both Linear Regression and Random Forest. Linear Regression got a lower RMSE. Random Forest overfit. That was a good reminder more complex doesn't always mean better. Sometimes the data is just... linear. The project also taught me that feature engineering matters more than model choice. Getting the right features in lag variables, rolling averages, trend components made a bigger difference than switching algorithms. GitHub link in the comments 👇 #MachineLearning #Python #SalesForecasting #DataScience #OpenToWork

All-in-One NumPy Guide. This guide includes: • Introduction to NumPy & array fundamentals • Indexing, slicing, and reshaping • Broadcasting and vectorization • Mathematical & statistical operations • And many more.... This “all-in-one” resource is designed to build a strong foundation in numerical computing and help apply NumPy effectively in Data Science and Machine Learning projects. """I’d really appreciate your feedback and would love to connect with professionals in the data field!""" #NumPy #Python #DataScience #MachineLearning #DataAnalytics #OpenToWork #Portfolio

📊 Pandas in Python – Making Data Simple & Powerfu Working with data doesn’t have to be complicated. With Pandas, we can easily clean, analyze, and manipulate data in just a few lines of code. From handling missing values to performing quick analysis, Pandas is an essential tool for anyone stepping into data science and machine learning. 🔹 Key Takeaways: • Two powerful structures: Series & DataFrame • Easy data handling (CSV, Excel, JSON) • Fast filtering, sorting, and analysis • Perfect for real-world datasets 💡 Whether you're a student or an aspiring data scientist, mastering Pandas can significantly boost your productivity and problem-solving skills. 🚀 Learning step by step and sharing the journey! #Python #Pandas #DataScience #MachineLearning #AI #Programming #Learning #Tech #StudentLife

🚀 Project Completed: Sales Data Analysis I analyzed a sales dataset using Python to identify revenue trends and top-performing products. 📊 Key Insights: Total revenue calculated Best-selling product identified Data visualized using graphs 🛠 Tools Used: Python, Pandas, Matplotlib This project helped me understand real-world data analysis workflow. #DataAnalytics #Python #Learning #OpenToWork

RMSE vs MAE — I was confused about this for a while. Here's the simple version. Both measure how wrong your model's predictions are. MAE — just takes the average of all errors. Simple, easy to understand. RMSE — punishes big mistakes more. One really bad prediction? RMSE will catch it. So when do you use which? Use MAE when all errors are roughly equal in importance. Use RMSE when big errors are a serious problem — like predicting sales, where one massive wrong forecast can hurt the business. I used RMSE in my sales forecasting project for exactly this reason. Got an RMSE of ~13,751 with Linear Regression — which actually beat Random Forest on the same data. Sometimes the simple model wins. That was a good lesson. #DataScience #MachineLearning #Python #LearningInPublic #OpenToWork

I’m excited to share one of the projects I worked on during my learning journey. 🔍 Problem: Predicting real estate prices based on historical data can help buyers and sellers make better decisions. 💡 Solution: I developed a Machine Learning model that analyzes property data and predicts prices using regression techniques. 🛠️ Tech Stack: Python | Machine Learning | Data Preprocessing | Regression Models 📊 What I did: • Collected and cleaned historical data • Performed Exploratory Data Analysis (EDA) • Applied regression algorithms for prediction • Evaluated model performance 📈 What I learned: • Importance of clean data • How ML models behave in real-world scenarios • Basics of model evaluation and improvement This project helped me strengthen my understanding of Data Science and Machine Learning. I’m currently improving my skills further and working on more projects. 👉 I’d love to hear your feedback and suggestions! #MachineLearning #DataScience #Python #Projects #LearningJourney #OpenToWork

Most people jump straight into building models. I’m learning to fix the data first. Today’s focus: Data Cleaning in Python 🧹 Here’s the reality — even the best algorithms fail with messy data. So I worked on: ✔️ Handling missing numeric values using mean ✔️ Filling categorical gaps with mode ✔️ Verifying data integrity before moving forward Simple steps… but they make a massive difference. What stood out to me: 👉 Data cleaning isn’t “boring prep work” — it’s where real analysis begins 👉 Small improvements in data quality can outperform complex models 👉 Clean data = reliable insights I’m starting to see that data science is less about fancy models and more about asking: “Can I trust this data?” 📊 This is part of my hands-on journey into data analysis and machine learning 📈 Focus: Building strong fundamentals, one step at a time If you’re in data or learning it — what’s one cleaning step you never skip? #DataScience #Python #DataCleaning #MachineLearning #Analytics #LearningInPublic #DataAnalytics #TechJourney #Unlox #GirishKumar

Data Analysis is not just about tools. Here are a few things I’m learning in my journey: • Cleaning data takes more time than analysis • Asking the right question is more important than using complex methods • Simple dashboards are often more useful than complex ones • Understanding the business problem matters more than coding • Most insights come from exploring, not from predefined steps • Consistency in practice beats random learning Right now, I’m focusing on: • Improving Python for data analysis • Practicing real datasets • Building dashboards step by step Still learning. Still improving. If you're on the same path, keep going. #datascience #dataanalysis #python #learning #students

Most people think data analysis starts with tools. Excel. Python. SPSS. Machine Learning. It doesn’t. It starts with a question. And this is where many get it wrong. Because a weak question will always produce weak insights, no matter how advanced your analysis is. I’ve seen projects where everything looked “technically correct”… but the conclusions made no real sense. Not because the data was bad. But because the question behind the analysis was shallow. Good analysis is not about running models. It’s about thinking clearly before you touch the data. So before your next project, ask yourself: Are you asking a question that actually matters… or just one that is easy to analyze? #HPAnalytics #DataAnalysis #Research #MachineLearning #CriticalThinking