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@morganred_ent: One tired lassy, goodnight all x #tiktokshop #glasgowgirl #livingroomdecor #fyp

Morgan Red
Morgan Red
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Wednesday 04 November 2026 00:37:34 GMT
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candicebishop40
candy :
luv your suite is it tiktok shop hun ?x
2026-11-04 03:37:06
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🚀 3 MACHINE LEARNING PROJECTS YOU CAN BUILD IN ONE WEEKEND No months of preparation. No complicated research papers. Pick a dataset, build a model, evaluate it, and turn it into a working project. 💻 1️⃣ CUSTOMER CHURN PREDICTOR 📉 Problem: Predict which customers are likely to stop using a service. Build these features: → Clean and explore customer data → Train Logistic Regression and Random Forest → Compare Precision, Recall, and F1-Score → Explain important features using SHAP → Create an interactive Streamlit dashboard Tech Stack: Python, Pandas, Scikit-learn, SHAP, Streamlit 🎯 Portfolio upgrade: Explain why a customer may leave and suggest retention actions. 2️⃣ HOUSE PRICE PREDICTOR 🏠 Problem: Estimate property prices using features such as location, size, and number of rooms. Build these features: → Analyze price distributions → Handle missing values and outliers → Encode categorical variables → Train Linear Regression and Random Forest → Evaluate with MAE, RMSE, and R² → Build a simple price prediction interface Tech Stack: Python, Pandas, Scikit-learn, Matplotlib, Streamlit 🎯 Portfolio upgrade: Compare model predictions with actual prices and explain prediction errors. 3️⃣ CREDIT CARD FRAUD DETECTOR 💳 Problem: Identify potentially fraudulent transactions. Build these features: → Explore class imbalance → Establish a baseline model → Compare Logistic Regression and Random Forest → Test class weighting or resampling → Evaluate Precision, Recall, and PR-AUC → Display suspicious transactions in a dashboard Tech Stack: Python, NumPy, Pandas, Scikit-learn, Streamlit 🎯 Portfolio upgrade: Explain why accuracy alone can be misleading when fraud cases are rare. 🗓️ YOUR WEEKEND BUILD PLAN SATURDAY * Morning: Dataset + data cleaning * Afternoon: EDA + feature engineering * Evening: Train baseline models SUNDAY * Morning: Evaluate + improve the model * Afternoon: Build the interface * Evening: Write README + upload to GitHub 🔥 THE SECRET TO A STRONG ML PORTFOLIO Don’t just upload a notebook. Include: ✅ Problem statement ✅ Dataset source ✅ Reproducible code ✅ Evaluation metrics ✅ Error analysis ✅ Working demo ✅ GitHub README One weekend. One finished project. One more piece of proof that you can build with ML. Save this roadmap and choose your first project. 🚀 #MachineLearning #MLProjects #DataScience #creatorsearchinsights #datascience
🚀 3 MACHINE LEARNING PROJECTS YOU CAN BUILD IN ONE WEEKEND No months of preparation. No complicated research papers. Pick a dataset, build a model, evaluate it, and turn it into a working project. 💻 1️⃣ CUSTOMER CHURN PREDICTOR 📉 Problem: Predict which customers are likely to stop using a service. Build these features: → Clean and explore customer data → Train Logistic Regression and Random Forest → Compare Precision, Recall, and F1-Score → Explain important features using SHAP → Create an interactive Streamlit dashboard Tech Stack: Python, Pandas, Scikit-learn, SHAP, Streamlit 🎯 Portfolio upgrade: Explain why a customer may leave and suggest retention actions. 2️⃣ HOUSE PRICE PREDICTOR 🏠 Problem: Estimate property prices using features such as location, size, and number of rooms. Build these features: → Analyze price distributions → Handle missing values and outliers → Encode categorical variables → Train Linear Regression and Random Forest → Evaluate with MAE, RMSE, and R² → Build a simple price prediction interface Tech Stack: Python, Pandas, Scikit-learn, Matplotlib, Streamlit 🎯 Portfolio upgrade: Compare model predictions with actual prices and explain prediction errors. 3️⃣ CREDIT CARD FRAUD DETECTOR 💳 Problem: Identify potentially fraudulent transactions. Build these features: → Explore class imbalance → Establish a baseline model → Compare Logistic Regression and Random Forest → Test class weighting or resampling → Evaluate Precision, Recall, and PR-AUC → Display suspicious transactions in a dashboard Tech Stack: Python, NumPy, Pandas, Scikit-learn, Streamlit 🎯 Portfolio upgrade: Explain why accuracy alone can be misleading when fraud cases are rare. 🗓️ YOUR WEEKEND BUILD PLAN SATURDAY * Morning: Dataset + data cleaning * Afternoon: EDA + feature engineering * Evening: Train baseline models SUNDAY * Morning: Evaluate + improve the model * Afternoon: Build the interface * Evening: Write README + upload to GitHub 🔥 THE SECRET TO A STRONG ML PORTFOLIO Don’t just upload a notebook. Include: ✅ Problem statement ✅ Dataset source ✅ Reproducible code ✅ Evaluation metrics ✅ Error analysis ✅ Working demo ✅ GitHub README One weekend. One finished project. One more piece of proof that you can build with ML. Save this roadmap and choose your first project. 🚀 #MachineLearning #MLProjects #DataScience #creatorsearchinsights #datascience


😂😂To Manchester united fans and Manchester United fans only have a lovely week 😂💔🥳tungonje Sunday ❤️🤲
😂😂To Manchester united fans and Manchester United fans only have a lovely week 😂💔🥳tungonje Sunday ❤️🤲
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Throwback from 2021! #catwalk #catluminati
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SkinGenie Face Blotting Tissue #skingenie #faceblottingpaper #blottingtissue
Repoztii🔥🫡 #somalitiktok #sanagkhatumo #sanagtii
Repoztii🔥🫡 #somalitiktok #sanagkhatumo #sanagtii

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