Sentiment Analysis of Social Media Data with Python

📊 Student Performance Predictor Built a regression model to estimate student GPA using different ML techniques. The project involved proper data cleaning, exploratory data analysis, and selecting the most impactful features. Compared Linear Regression and Random Forest, where Random Forest performed better in terms of accuracy. Some key factors influencing performance: Studytimeweekly, Absences, .... etc. 🛠 Tools: Python, Pandas, Scikit-learn, Plotly #MachineLearning #DataScience #Python #StudentProject #MLProjects

📊 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

Still Googling Pandas syntax every time you work on a project? . . . . I created a one-page Pandas Cheat Sheet covering the most used commands: read_csv() • groupby() • merge() • fillna() • drop_duplicates() Save this before your next project Which topic should I cover next: NumPy / Statistics / ML Metrics ? #Pandas #Python #DataAnalytics #DataScience #MachineLearning #Analytics #InterviewPreparation

𝐎𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐮𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞𝐝 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐞𝐬 While exploring a dataset in Python recently, I noticed how often real datasets contain missing values. At first it seems like a small issue, but it can actually affect the entire analysis. Using pandas functions like isnull() and fillna() made it easier to detect and handle those gaps before doing any calculations or visualizations. It made me realize that a big part of data analysis isn’t just analyzing the data — it’s preparing the data properly so the results actually make sense. Still learning, but these small steps are starting to make the workflow clearer. #Python #Pandas #DataAnalytics #DataCleaning

🚀 Machine Learning Project – California Housing Price Prediction I recently completed a mini project on House Price Prediction using the California Housing dataset. 🔹 Tools Used: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn 🔹 Model: Linear Regression 🔹 Process: • Performed Exploratory Data Analysis (EDA) • Checked feature correlations and distributions • Split data into training and testing sets • Built and evaluated a Linear Regression model 📊 Evaluation Metrics: • MAE (Mean Absolute Error) • RMSE (Root Mean Squared Error) • R² Score This project helped me understand how machine learning models can be used to predict real-world data like housing prices. 🔗 GitHub Repository: https://lnkd.in/gWgeZVUr #MachineLearning #DataScience #Python #LinearRegression #LearningJourney

𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗠𝗟 𝗠𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗬𝗲𝗹𝗹𝗼𝘄𝗯𝗿𝗶𝗰𝗸! 📊 Yellowbrick is a Python library that provides useful visualizations for machine learning models. For example, regression models can be visualized with a prediction error plot or Cook's distance, whereas ROC/AUC curves and the confusion matrix are suitable for classification models. Furthermore, Yellowbrick can be installed by itself, or alternatively used with the PyCaret library that integrates its functionality. Have you ever utilized Yellowbrick to visualize machine learning models? Visit the links below for more information, and make sure to follow me for regular data science content! 𝗬𝗲𝗹𝗹𝗼𝘄𝗯𝗿𝗶𝗰𝗸 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/enK2fQ2D 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

🚀 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

Data cleaning shouldn't be a headache. 🐍💻 Most of a Data Analyst's time isn't spent building models—it’s spent cleaning the mess. I’ve put together a minimalist Data Cleaning in Python Cheat sheet covering the essential steps to get your datasets "analysis-ready" in minutes. What’s inside: ✅ Standardizing formats & strings ✅ Handling duplicates & missing values ✅ Filtering outliers with the IQR method ✅ Quick data exploration commands Whether you're using Pandas for the first time or just need a quick syntax refresher, keep this one bookmarked.  #DataScience #DataAnalytics #Python #Pandas #DataCleaning #CodingTips #MachineLearning

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