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Water Quality Prediction System – A Smart Web-Based ML Application
I made this Water Quality Prediction System, which is basically a website made with Python and Flask that uses machine learning to tell if water
💧 Water Quality Prediction – Data Science Project
A machine learning–based solution to assess and predict the safety of water based on physical and chemical parameters. An essential project for environmental monitoring and public health applications.
🔬 Project Highlights
• Predicts water potability using supervised ML models
• Involves data cleaning, visualization & feature engineering
• Algorithms: Logistic Regression, Random Forest, SVM, etc.
• Built with Python, Pandas, Scikit-learn, Matplotlib
• Ideal for environmental science and public safety solutions
🔗 Explore Full Project:
Water Quality Prediction – View Now
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A machine learning–based solution to assess and predict the safety of water based on physical and chemical parameters. An essential project for environmental monitoring and public health applications.
🔬 Project Highlights
• Predicts water potability using supervised ML models
• Involves data cleaning, visualization & feature engineering
• Algorithms: Logistic Regression, Random Forest, SVM, etc.
• Built with Python, Pandas, Scikit-learn, Matplotlib
• Ideal for environmental science and public safety solutions
🔗 Explore Full Project:
Water Quality Prediction – View Now
📢 Explore more impactful data science projects:
🔗 https://t.me/Projectwithsourcecodes
#WaterQuality #DataScienceProject #MLProject #EnvironmentalTech #PythonProject #WaterPotability #CleanWater #MachineLearning #Sustainability #projectwithsourcecodes #PublicHealthAI
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Best Smart Product Review Sentiment Analysis with Real-Time Insights
Product Review Sentiment Analysis Knowing what people actually think about a product is super important for companies that want to make
📣 Product Review Sentiment Analysis – Python Project
Leverage natural language processing to analyze product review sentiment and classify user feedback as positive, negative, or neutral. A valuable project for e-commerce platforms and NLP learners.
🔍 Key Features
• Text preprocessing and sentiment scoring
• Classification with Naive Bayes / Logistic Regression
• Visualization of sentiment distribution and analytics
• Built using Python, Pandas, Scikit-learn, Matplotlib
• Ideal for e‑commerce analytics and portfolio projects
🔗 Explore Project:
Product Review Sentiment Analysis – View Now
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🔗 https://t.me/Projectwithsourcecodes
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Leverage natural language processing to analyze product review sentiment and classify user feedback as positive, negative, or neutral. A valuable project for e-commerce platforms and NLP learners.
🔍 Key Features
• Text preprocessing and sentiment scoring
• Classification with Naive Bayes / Logistic Regression
• Visualization of sentiment distribution and analytics
• Built using Python, Pandas, Scikit-learn, Matplotlib
• Ideal for e‑commerce analytics and portfolio projects
🔗 Explore Project:
Product Review Sentiment Analysis – View Now
📢 More practical AI & Python projects available at:
🔗 https://t.me/Projectwithsourcecodes
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Best Insurance Claim Prediction Web App Using Machine Learning
OverviewThe Insurance Claim Prediction App is a web tool made with Streamlit that uses machine learning to guess how much someone’s
📊 Insurance Claim Prediction – Data Science Project
Build a smart system that predicts whether a customer is likely to claim insurance using historical data and machine learning models. Perfect for those exploring data science in the insurance domain.
🚀 Key Highlights:
✅ Logistic Regression, Random Forest, and XGBoost models
✅ Data preprocessing, encoding & feature selection
✅ Imbalanced data handling techniques
✅ Performance evaluation using accuracy, precision, recall, F1-score
✅ Clean Python code with visualization
🔗 Project Link:
Insurance Claim Prediction
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Build a smart system that predicts whether a customer is likely to claim insurance using historical data and machine learning models. Perfect for those exploring data science in the insurance domain.
🚀 Key Highlights:
✅ Logistic Regression, Random Forest, and XGBoost models
✅ Data preprocessing, encoding & feature selection
✅ Imbalanced data handling techniques
✅ Performance evaluation using accuracy, precision, recall, F1-score
✅ Clean Python code with visualization
🔗 Project Link:
Insurance Claim Prediction
📦 For more projects:
🔗 https://t.me/Projectwithsourcecodes
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