💼 Employee Salary Prediction – Python/ML Project
Build a machine learning model to estimate employee salaries from features like experience, education, and skills. Great for developers working on HR analytics and predictive modeling.
Key Features
• Predict salaries based on variables such as years of experience, education, and job title
• Utilizes regression models like Linear Regression, Decision Trees, or Random Forest
• Visualizes actual vs. predicted salary comparisons through charts
• Built with Python libraries including pandas, scikit-learn, and visualization tools like Matplotlib/Seaborn
🔗 Explore the full project here:
https://updategadh.com/data-science-project/employee-salary-prediction/
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Build a machine learning model to estimate employee salaries from features like experience, education, and skills. Great for developers working on HR analytics and predictive modeling.
Key Features
• Predict salaries based on variables such as years of experience, education, and job title
• Utilizes regression models like Linear Regression, Decision Trees, or Random Forest
• Visualizes actual vs. predicted salary comparisons through charts
• Built with Python libraries including pandas, scikit-learn, and visualization tools like Matplotlib/Seaborn
🔗 Explore the full project here:
https://updategadh.com/data-science-project/employee-salary-prediction/
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
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🧠 Brain Stroke Prediction – Python/ML Project
Forecast the risk of brain stroke using medical data and machine learning techniques. Ideal for developers building healthcare analytics tools with predictive capabilities.
Key Features
• Predicts stroke risk based on health indicators like age, BMI, smoking, and comorbidities
• Utilizes machine learning models such as Logistic Regression, Random Forest, or XGBoost
• Visual comparisons of predicted risk vs actual outcome
• Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
🔗 Explore the full project here:
Brain Stroke Prediction – View Project
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https://t.me/Projectwithsourcecodes
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Forecast the risk of brain stroke using medical data and machine learning techniques. Ideal for developers building healthcare analytics tools with predictive capabilities.
Key Features
• Predicts stroke risk based on health indicators like age, BMI, smoking, and comorbidities
• Utilizes machine learning models such as Logistic Regression, Random Forest, or XGBoost
• Visual comparisons of predicted risk vs actual outcome
• Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
🔗 Explore the full project here:
Brain Stroke Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
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🤖 Healthcare Chatbot Using Python – Python Project
Build a friendly, intelligent chatbot to assist users with basic health advice, symptom checks, and general medical guidance. Ideal for developers exploring NLP, user interaction, and healthcare tech.
Key Features
• Engage users in natural language conversations for health-related queries
• Use Python NLP libraries such as NLTK, spaCy, or transformers
• Handle symptoms, suggestions, and common medical responses
• Modular architecture – easy to extend for knowledge base expansion
• Built with Python and structured for chat interface integration
🔗 Explore the full project here:
Healthcare Chatbot Using Python – View Project
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Build a friendly, intelligent chatbot to assist users with basic health advice, symptom checks, and general medical guidance. Ideal for developers exploring NLP, user interaction, and healthcare tech.
Key Features
• Engage users in natural language conversations for health-related queries
• Use Python NLP libraries such as NLTK, spaCy, or transformers
• Handle symptoms, suggestions, and common medical responses
• Modular architecture – easy to extend for knowledge base expansion
• Built with Python and structured for chat interface integration
🔗 Explore the full project here:
Healthcare Chatbot Using Python – View Project
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🤝 Movie Review Sentiment Analysis – Python / ML
Ek machine learning project jo movie reviews ko analyze karta hai aur unka sentiment (positive/negative) predict karta hai. Yeh NLP techniques aur classification models ka use karke user opinions ko automatically interpret karta hai.
Key Features
• Text preprocessing: tokenization, cleaning, stopwords removal
• Feature extraction: TF-IDF or word embeddings
• Classification model: Logistic Regression / SVM / Naive Bayes
• Prediction interface: input review → output sentiment
• Model evaluation metrics: accuracy, precision, recall, confusion matrix
🔗 Explore the full project here:
Movie Review Sentiment Analysis – View Project
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https://t.me/Projectwithsourcecodes
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Ek machine learning project jo movie reviews ko analyze karta hai aur unka sentiment (positive/negative) predict karta hai. Yeh NLP techniques aur classification models ka use karke user opinions ko automatically interpret karta hai.
Key Features
• Text preprocessing: tokenization, cleaning, stopwords removal
• Feature extraction: TF-IDF or word embeddings
• Classification model: Logistic Regression / SVM / Naive Bayes
• Prediction interface: input review → output sentiment
• Model evaluation metrics: accuracy, precision, recall, confusion matrix
🔗 Explore the full project here:
Movie Review Sentiment Analysis – View Project
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🤝 Student Exam Performance Prediction – Data Science / ML
A machine learning project that predicts how students are likely to perform in their exams based on past data, study hours, demographics, etc.
Key Features
• Data preprocessing: handle missing values, normalization, encoding categorical variables
• Feature selection to identify major factors affecting performance
• Model training: regression/classification models like Random Forest, Decision Trees, or Logistic Regression
• Evaluation metrics: accuracy, precision, recall, F1-score, possibly confusion matrix
• Prediction interface: input student details → output expected performance
🔗 Explore the full project here:
Student Exam Performance Prediction – View Project
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A machine learning project that predicts how students are likely to perform in their exams based on past data, study hours, demographics, etc.
Key Features
• Data preprocessing: handle missing values, normalization, encoding categorical variables
• Feature selection to identify major factors affecting performance
• Model training: regression/classification models like Random Forest, Decision Trees, or Logistic Regression
• Evaluation metrics: accuracy, precision, recall, F1-score, possibly confusion matrix
• Prediction interface: input student details → output expected performance
🔗 Explore the full project here:
Student Exam Performance Prediction – View Project
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🤝 Bike Price Prediction – Data Science / ML
A machine learning project that predicts the resale price of bikes using data like brand, model, year, engine capacity, and kilometers driven. It uses Flask for deployment and XGBoost as the main model for accurate predictions.
Key Features
• Flask web interface for user input
• XGBoost model for price prediction
• Data preprocessing and feature selection
• High model accuracy on test data
• Clean and responsive result display
🔗 Explore the full project here:
Bike Price Prediction – View Project
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🤝 Bike Price Prediction – Data Science / ML
A machine learning project that predicts the resale price of bikes using data like brand, model, year, engine capacity, and kilometers driven. It uses Flask for deployment and XGBoost as the main model for accurate predictions.
Key Features
• Flask web interface for user input
• XGBoost model for price prediction
• Data preprocessing and feature selection
• High model accuracy on test data
• Clean and responsive result display
🔗 Explore the full project here:
Bike Price Prediction – View Project
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Update Gadh
Best Game Recommender System Using Machine Learning
A simple project on the Game Recommender System that leverages data-driven techniques to suggest games based on user preferences
🤝 Game Recommender System – Python / Streamlit
A machine learning-based project that recommends video games to users based on their interests and preferences. It uses collaborative and content-based filtering techniques to deliver personalized game suggestions through an interactive Streamlit web app.
Key Features
• Personalized game recommendations
• Collaborative & content-based filtering models
• Real dataset integration for accurate results
• Streamlit-based interactive user interface
• Modular and easy-to-customize code structure
🔗 Explore the full project here:
Game Recommender System – View Project
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https://t.me/Projectwithsourcecodes
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A machine learning-based project that recommends video games to users based on their interests and preferences. It uses collaborative and content-based filtering techniques to deliver personalized game suggestions through an interactive Streamlit web app.
Key Features
• Personalized game recommendations
• Collaborative & content-based filtering models
• Real dataset integration for accurate results
• Streamlit-based interactive user interface
• Modular and easy-to-customize code structure
🔗 Explore the full project here:
Game Recommender System – View Project
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https://t.me/Projectwithsourcecodes
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