Alexandre Viegas’ Post

Still spending all your time looking in the rearview mirror? Dashboards are great for showing what happened, but predictive modeling is where you start answering what happens next. To move from descriptive to predictive, focus on these three shifts: - Mindset: Move from reporting fixed numbers to calculating probabilities and future trends. - Tools: Go beyond SQL and BI tools by building foundational skills in Python and Machine Learning. - Data: Stop just cleaning data for display and start engineering features that reveal hidden patterns. It’s a big step, but it’s the most rewarding transition an analyst can make. We're here to help you bridge that gap with hands-on projects. Start your AI career with Dallas Data Science Academy. Register today at: https://lnkd.in/gR_r4aAr #DataScience #AI #Bootcamp #CareerGrowth

🚀 Day 130 of My Data Science Journey 🎯 Customer Churn Prediction using Machine Learning I’ve completed another exciting ML project where I built a model to predict whether a customer will leave a telecom service or stay. --- 🔍 Problem Statement Predict customer churn based on usage patterns and customer-related features. --- 🤖 Model Used • Random Forest Classifier 📊 Accuracy ✔ ~83% --- 🛠️ Tech Stack • Python • Pandas & NumPy • Scikit-learn • Matplotlib & Seaborn --- 🔑 Key Steps 1️⃣ Exploratory Data Analysis (EDA) 2️⃣ Handling missing & inconsistent values 3️⃣ Label Encoding & One-Hot Encoding (pd.get_dummies) 4️⃣ Model training & evaluation 5️⃣ Feature Importance Analysis --- 💡 Biggest Lesson Feature Importance is a game changer — understanding which features drive churn is often more valuable than the prediction itself. --- 📌 Project Insight This project improved my understanding of classification models and how insights can drive real business decisions. -- #Day130 #MachineLearning #Python #DataScience #CustomerChurn #RandomForest #sklearn #LearningInPublic #MLEngineer #AI

📊 Still going strong on the Data Science grind. Up next on the list - Statistics for DSML. What I've been working on: I just completed my first hands-on practice in Statistics: Frequency Distribution- implemented entirely in Python using Pandas, NumPy, Matplotlib, and Seaborn. Here's what I explored across 10 core concepts: 1. Frequency & Frequency Distribution 2. Relative Frequency 3. Cumulative Frequency 4. Contingency Tables 5. Covariance & Correlation 6. KDE / Distribution plots 7. Types of Distribution (Normal, Binomial, Poisson, Exponential, Uniform) 8. Central Limit Theorem The patterns I found were genuinely eye-opening; a near-zero correlation between hours studied and scores reminded me that data rarely lies, but it always has a story behind it. 📌Something extra I practiced: I've also been learning Prompt Engineering lately, and I used it to generate the PowerPoint presentation you see here, directly from Claude AI. I applied the Craft Technique to structure my prompts, defining Role, Context, Action, Format, and Constraints, and the quality of the output was a significant step up from generic prompting. It's been a great reminder that how you ask matters as much as what you ask. Building both technical and AI-communication skills in parallel feels like the right move for anyone entering this field. 🔗 GitHub Repo: https://lnkd.in/g8UuxSBP A huge thank you to my mentor, Yash Wadpalliwar, and Fireblaze AI School, Fireblaze AI School - Training and Placement Cell for the structured guidance and constant encouragement. This progress wouldn't look the same without that support. If you're on a similar journey of learning statistics, Python, or just figuring out where to start in Data Science, let's connect. #DataScience #Statistics #Python #MachineLearning #PromptEngineering #LearningInPublic #DSML #FrequencyDistribution #StudentLife #DataAnalytics #LinkedInCommunity

🚢 Excited to share my latest Machine Learning project: Titanic Survival Prediction System I built an end-to-end ML project to predict whether a passenger would survive the Titanic disaster based on historical passenger data. This project helped me strengthen my practical skills in data science and model deployment. 🔍 What I worked on: ✅ Data Cleaning & Preprocessing ✅ Exploratory Data Analysis (EDA) ✅ Feature Engineering ✅ Logistic Regression Model Training ✅ Model Evaluation (Accuracy & Confusion Matrix) ✅ Web App Deployment using Streamlit / Flask 📊 Key Insights: Gender had a strong impact on survival chances Passenger class and fare were important factors Family size also influenced survival probability 🛠️ Tech Stack: Python | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn | Streamlit | Flask This project gave me hands-on experience in transforming raw data into actionable predictions and deploying a model as an interactive application. I’m continuing to grow my skills in Data Science, Machine Learning, and AI, and I’m excited to build more real-world projects. https://lnkd.in/gQJrKkK4 https://lnkd.in/g-aRdKbG #MachineLearning #DataScience #Python #AI #Streamlit #Flask #ScikitLearn #PortfolioProject #LinkedInLearning

🏡 House Price Prediction using Machine Learning (XGBoost) I’m excited to share my latest Machine Learning project developed as part of my training with #SkillinfyTechITSolutions Pvt. Ltd.🚀 This project focuses on predicting real estate prices using a regression-based machine learning model. It estimates house prices based on features such as Average Area Income, House Age, Number of Rooms, Number of Bedrooms, and Area Population. The model is built using XGBoost Regressor and follows an end-to-end machine learning workflow including data preprocessing, feature selection, model training, evaluation, and prediction. A simple CLI-based system is also implemented to take user inputs and generate real-time house price predictions. 📊 Model Performance R² Score: ~0.90 MAE: Low prediction error RMSE: Stable performance on test data ⚙️ Tools & Technologies Python, Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, Joblib 🎯 Key Highlights ✔ End-to-end regression pipeline ✔ Model persistence using Joblib ✔ Real-time CLI prediction system ✔ Data visualization (Actual vs Predicted) ✔ Performance evaluation using standard metrics This project helped me strengthen my understanding of real-world regression modeling, feature engineering, and machine learning deployment concepts. 🔗 GitHub Repository: https://lnkd.in/gRnMkf9D #MachineLearning #DataScience #Python #XGBoost #Skillinfytechitsolutions #AI #MLProject #RegressionModel

Machine Learning/Artificial Intelligence  Day 12.  Today, I worked on a large sales dataset and ran 7 different analyses to uncover hidden patterns.What I did:First, I loaded the dataset into Jupyter using pandas. The data had thousands of rows with sales records across different regions, products, and shipping methods.Then I asked specific questions:1. Which region made the most sales?2. Which product sold the highest quantity?3. Which ship mode had the most delays?4. How do sales trend across different months?5. Which product category brings in the most revenue?6. Is there a relationship between discount and profit?7. Which region prefers which ship mode?Tools I used:· pandas – to clean, filter, and group the data· seaborn & matplotlib – to create histograms, bar charts, pie charts, and line graphs· Jupyter – for writing and testing my code· Google Colab – to share the notebook and collaborate· GitHub – to save, track, and share my workWhat I found:One region alone made up 40% of total sales. One product sold three times more than others. And the fastest ship mode actually had the most late deliveries – a surprising insight.Why this matters:For AI/ML, understanding your data before building models is half the work. A good chart can save hours of wrong assumptions. And sharing work on GitHub keeps everything organized and open for feedback.What I learned today:EDA is not just about making charts. It is about asking the right questions. Visualization is not just about pretty colors. It is about telling a clear story. Collaboration is not just about sharing files. It is about making your work useful to others.Learning step by step, staying consistent every day!#M4ACE LearningChallenge#LearningInPublic#30DaysOfAIML#EDA #DataVisualization #Python #pandas #seaborn #matplotlib #GitHub

I recently worked on a few data science projects involving 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧, 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠, and 𝐭𝐢𝐦𝐞 𝐬𝐞𝐫𝐢𝐞𝐬 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 using Python and common machine learning libraries. Here’s a brief overview of what I did: • Task 1: 𝐁𝐚𝐧𝐤 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 – 𝐓𝐞𝐫𝐦 𝐃𝐞𝐩𝐨𝐬𝐢𝐭 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 Built classification models to predict customer subscription behavior and evaluated performance using metrics like F1-score and ROC curve. Also used SHAP for basic model interpretability. GitHub: https://lnkd.in/dpbpX2FF • 𝐓𝐚𝐬𝐤 𝟐: 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 Applied K-Means clustering on mall customer data and used PCA for visualization. Based on the clusters, I derived basic marketing insights for each segment. GitHub: https://lnkd.in/dHc56spX • 𝐓𝐚𝐬𝐤 𝟑: 𝐄𝐧𝐞𝐫𝐠𝐲 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Worked with household power consumption data, engineered time-based features, and compared forecasting models including ARIMA, Prophet, and XGBoost. GitHub: https://lnkd.in/duy43Wvg 𝐊𝐞𝐲 𝐚𝐫𝐞𝐚𝐬 𝐜𝐨𝐯𝐞𝐫𝐞𝐝: Machine learning (classification & clustering), time series forecasting, feature engineering, and model evaluation. #DataScience #MachineLearning #Python #AI #DataAnalytics #TimeSeriesAnalysis #Clustering #Classification #XGBoost #Pandas #ScikitLearn DevelopersHub Corporation©

🚀 Excited to share my latest Machine Learning project! I recently worked on a **California Housing Price Prediction** model using Linear Regression. This project helped me strengthen my understanding of the complete ML workflow — from data exploration to model evaluation and deployment. 🔍 Key highlights: • Performed data analysis and visualization using Pandas, Matplotlib & Seaborn • Explored feature correlations and distributions • Built and trained a Linear Regression model using Scikit-learn • Evaluated performance using MAE, RMSE, and R² score • Visualized predictions and residuals for better insights • Saved and reloaded the trained model using Joblib 📊 This project gave me hands-on experience in: Data preprocessing | Model training | Evaluation metrics | Visualization 🔗 Check out the full project here: https://lnkd.in/gcHN8pQY I’m continuously learning and exploring more in Machine Learning and Data Science. Open to feedback and suggestions! #MachineLearning #DataScience #Python #LinearRegression #AI #LearningJourney #Projects #GitHub

Day 16/30 of my Data Analyst + AI journey 🚀 Today I moved one step closer to real-world data analysis. I focused on cleaning data using Pandas and explored advanced concepts of Object-Oriented Programming. 👉 What I learned today: 🔹 Data Cleaning with Pandas Real-world data is never perfect… it contains missing values and duplicates. 👉 Handling Missing Values df.isnull().sum() df.dropna() df.fillna(0) 👉 Removing Duplicates df.drop_duplicates() 👉 Why Data Cleaning? • Improves data quality • Ensures accurate analysis • Makes data reliable 🔹 OOP Advanced Concepts 👉 Inheritance class Person: def greet(self): print("Hello") class Student(Person): pass s = Student() s.greet() 👉 Polymorphism class Dog: def sound(self): print("Bark") class Cat: def sound(self): print("Meow") for animal in [Dog(), Cat()]: animal.sound() 👉 What I understood: • Clean data = correct results • OOP makes code scalable • Both are essential in real-world projects How I used AI today: 👉 Practiced data cleaning 👉 Understood OOP concepts 👉 Fixed errors quickly 💡 Key Learning: Data is only useful when it’s clean… And code is only powerful when it’s well-structured. Today felt like real Data Analyst work 📊🔥 If you’re also learning Data Analytics or Python, comment “IN” — let’s grow together 🤝 #Python #Pandas #OOP #DataAnalytics #AI #Learning #Consistency #Day16

Lately I have been spending a lot of time getting comfortable with data preprocessing and exploratory data analysis (EDA), and it has honestly changed how I see data. I used to think the real magic was in building models but I am starting to realize the real work happens before that. Cleaning data, handling missing values and encoding variables may not look exciting but they make all the difference. EDA has been even more interesting for me. It feels less like analysis and more like getting to know the data. You begin to see patterns, relationships and even hidden issues you would have otherwise missed. One tool I am really enjoying right now is the correlation heat map. It gives a clear visual of how variables relate to each other and helps me make better decisions on what to keep or drop. My biggest takeaway so far is, understand your data first, everything else becomes easier after that. Still learning Still building #DataScience #MachineLearning #EDA #DataAnalysis #DataPreprocessing #Analytics #LearningJourney #AI #Tech #Python #DataVisualization #CareerGrowth #SkillBuilding #TechJourney #FutureOfWork