Abishek T’s Post

📊 My First Machine Learning Project — CGPA vs Salary Prediction! I built a Linear Regression model in Python that predicts student salary packages based on CGPA. 🔍 What I did: ✅ Exploratory Data Analysis ✅ Trained a Linear Regression model ✅ Evaluated predictions with % error ✅ Visualized the regression line 🔧 Tools: Python | Pandas | Scikit-learn | Matplotlib 🔗 Full project on GitHub: https://lnkd.in/dEtZaUdm #MachineLearning #Python #DataScience #LinearRegression #FirstProject

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐦𝐚𝐝𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐦𝐨𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐦𝐞 While exploring datasets in Python recently, I spent some time understanding how correlation works between variables. Using pandas, it’s surprisingly easy to calculate a correlation matrix and see how different columns relate to each other. Sometimes two variables move together strongly, and sometimes there’s almost no relationship at all. What I found interesting is that correlations can quickly highlight patterns that might not be obvious just by looking at raw numbers. Still learning how to interpret these relationships properly, but it’s definitely making the analysis process more insightful. #Python #Pandas #DataAnalytics

I have been spending more time working with pandas in Python, and honestly, I didn’t realize how powerful it actually is. What started as basic data cleaning slowly turned into understanding how easily large datasets can be transformed, filtered, and structured with just a few lines of code. I’ve been exploring things like: → handling messy data → grouping and aggregations → preparing datasets before analysis And it’s starting to change how I look at data — not just from a reporting side, but how it’s actually processed behind the scenes. Still learning, but definitely enjoying the process of uncovering what pandas can really do. #Python #Pandas #DataAnalytics #Learning #DataEngineering

🚀 Hook: I started building my first interactive data dashboard using Python… and here’s what I’ve learned so far 👇 --- 💡 Caption: After working on my EDA tool, I decided to level up my skills by building a data dashboard. Right now, I’m in the process of building it using: - Python - Streamlit - Plotly So far, I’ve learned: ✅ How to load and clean data ✅ How to create basic charts ✅ How to structure a simple dashboard layout Still facing some issues while running the app — but solving them step by step 💪 This journey is teaching me one important thing: 👉 You don’t need to be perfect to start… you just need to start. --- 💬 If you’ve built dashboards before, any tips would be helpful! 👇 Follow me to see the final version soon. --- 🔥 Hashtags: #DataAnalytics #Python #LearningInPublic #Streamlit #Plotly #BeginnerJourney #BuildInPublic #Tech #AI #Projects

If you're working with data, mastering NumPy is non-negotiable. 📊 From array creation to linear algebra, this cheat sheet is a quick reminder of how powerful NumPy really is. Whether you're cleaning data, running statistical analysis, or building models — these functions are your daily toolkit. Save this for later… your future self will thank you. 😉 #DataScience #Python #NumPy #DataAnalytics #MachineLearning

🔥 While working with data, I noticed something interesting. The same dataset can lead to different conclusions depending on how it is visualized. 📊 Using Matplotlib and Seaborn in Python helped me see this clearly. Matplotlib gives more control to design charts the way we want. Seaborn helps create clean and structured visuals quickly. #DataAnalytics #Python #Matplotlib #Seaborn #DataVisualization

Are Matplotlib abstractions helping—or getting in the way? Let’s ask Cameron Riddell! In this week’s Cameron’s Corner, Cameron looks at the layers of abstraction in Matplotlib and how they shape the way we write plotting code. While higher-level interfaces can make things faster to write, they can also obscure what’s actually happening underneath. Learn: ✅ How Matplotlib’s abstraction layers are structured ✅ When higher-level APIs simplify your workflow ✅ Why dropping down a level can sometimes give you more control Read here: https://lnkd.in/gVJKvErq Do you prefer high-level plotting tools or working closer to Matplotlib’s core? Let us know how you approach it 👇 #Python #Matplotlib #DataViz #CameronsCorner

I used to write a lot of clumsy `if` statements just to group data. Checking if a key existed, then initializing a list, then appending. It felt clunky and repetitive. This simple Python trick lets you group any data points by category without boilerplate code, making your data prep for AI/ML much cleaner. It's perfect for aggregating model results by metric or sorting samples by class. 💡 What's your go-to Python trick for cleaning up data operations? #Python #PythonTips #MachineLearning #DataScience #Coding

We built a Spam Email Classifier as a group using Machine Learning in Python. What it does: Detects whether an email is spam or not. Dataset: 10,000 emails 🤖 Model: Random Forest Classifier Accuracy: 88.7% | F1-Score: 86% Using a dataset from kaggle https://lnkd.in/dNZfH4Fr Tools used: Python · Scikit-learn · Pandas · Matplotlib It is now on my github https://lnkd.in/drKeE_se #MachineLearning #Python #AI #DataScience #StudentProject

Today, I took a practical step into working with data using pandas. Here’s what I focused on: Understanding the basics of data manipulation Exploring how datasets are structured Performing simple operations on data To apply what I learned, I built a basic salary analyzer—a small project, but a strong start toward working with real-world datasets. This marks the shift from just learning syntax to actually working with data. More to come. #Python #DataAnalytics #Pandas #LearningInPublic #DataJourney #BuildInPublic