Forwarded from ProjectWithSourceCodes
📊 Employee Attrition Prediction – Python/ML Project
Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
• Trains models on employee demographics, performance, and engagement metrics
• Predicts attrition risk using algorithms like Logistic Regression or Random Forest
• Provides visual dashboards comparing predicted vs actual turnover trends
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Employee Attrition Prediction – View Project
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Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
• Trains models on employee demographics, performance, and engagement metrics
• Predicts attrition risk using algorithms like Logistic Regression or Random Forest
• Provides visual dashboards comparing predicted vs actual turnover trends
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Employee Attrition Prediction – View Project
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#EmployeeAttritionPrediction #PythonProject #MachineLearning #DataScience #HRAnalytics #PredictiveModeling #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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📉 Hotel Booking Cancellation Prediction – Python/ML Project
Predict hotel booking cancellations using historical reservation data and machine learning techniques. Ideal for developers working on predictive analytics in hospitality and travel sectors.
Key Features
• Analyzes guest profiles, booking channel, stay dates, and special requests
• Uses algorithms like Logistic Regression, Random Forest, or Gradient Boosting
• Visualizes actual vs predicted cancellation trends for clear insight
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Hotel Booking Cancellation Prediction – View Project
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#HotelBookingCancellationPrediction #PythonProject #MachineLearning #DataScience #HospitalityTech #PredictiveAnalytics #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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Predict hotel booking cancellations using historical reservation data and machine learning techniques. Ideal for developers working on predictive analytics in hospitality and travel sectors.
Key Features
• Analyzes guest profiles, booking channel, stay dates, and special requests
• Uses algorithms like Logistic Regression, Random Forest, or Gradient Boosting
• Visualizes actual vs predicted cancellation trends for clear insight
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Hotel Booking Cancellation Prediction – View Project
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📈 Marketing Campaign Demand Prediction – Python/ML Project
Predict the success of marketing campaigns and forecast demand using real business data. Ideal for developers building data-driven tools for sales and marketing strategy.
Key Features
• Analyzes customer demographics, campaign types, and prior purchasing behavior
• Uses regression or classification models like Random Forest, XGBoost, or Logistic Regression
• Visualizes prediction performance with charts comparing actual and forecasted demand
• Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
🔗 Explore the full project here:
Marketing Campaign Demand Prediction – View Project
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Predict the success of marketing campaigns and forecast demand using real business data. Ideal for developers building data-driven tools for sales and marketing strategy.
Key Features
• Analyzes customer demographics, campaign types, and prior purchasing behavior
• Uses regression or classification models like Random Forest, XGBoost, or Logistic Regression
• Visualizes prediction performance with charts comparing actual and forecasted demand
• Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
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Marketing Campaign Demand Prediction – View Project
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📊 Customer Personality Analysis – Python/ML Project
Understand customer personalities and behavior using machine learning and survey data. Ideal for developers exploring psychographics, segmentation, and marketing personas.
Key Features
• Analyzes survey or transaction data to infer personality traits
• Applies clustering or classification models (e.g., K-Means, Decision Tree)
• Visualizes customer segments and trait distributions
• Built with Python libraries like pandas, scikit-learn, and Matplotlib/Seaborn
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Customer Personality Analysis – View Project
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Understand customer personalities and behavior using machine learning and survey data. Ideal for developers exploring psychographics, segmentation, and marketing personas.
Key Features
• Analyzes survey or transaction data to infer personality traits
• Applies clustering or classification models (e.g., K-Means, Decision Tree)
• Visualizes customer segments and trait distributions
• Built with Python libraries like pandas, scikit-learn, and Matplotlib/Seaborn
🔗 Explore the full project here:
Customer Personality Analysis – View Project
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🧪 AI-Powered Fake News Detection – Python/ML Project
Combat misinformation with this advanced Fake News Detection tool. Ideal for developers diving into Natural Language Processing and machine learning applications.
Key Features
• Processes news text to detect authenticity—real vs fake
• Uses NLP techniques like TF-IDF, Word Embeddings, or BERT
• Classification models like Logistic Regression, SVM, or deep learning
• Visualizes prediction confidence and accuracy metrics
• Built with Python libraries: pandas, scikit-learn, NLTK/spacy, and possibly TensorFlow/Keras
🔗 Explore the full project here:
AI-Powered Fake News Detection – View Project
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Combat misinformation with this advanced Fake News Detection tool. Ideal for developers diving into Natural Language Processing and machine learning applications.
Key Features
• Processes news text to detect authenticity—real vs fake
• Uses NLP techniques like TF-IDF, Word Embeddings, or BERT
• Classification models like Logistic Regression, SVM, or deep learning
• Visualizes prediction confidence and accuracy metrics
• Built with Python libraries: pandas, scikit-learn, NLTK/spacy, and possibly TensorFlow/Keras
🔗 Explore the full project here:
AI-Powered Fake News Detection – View Project
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📊 Customer Segmentation Dashboard – Python/ML Project
Visualize and analyze customer groups using machine learning. This project is perfect for developers building segmentation tools and marketing analytics dashboards.
Key Features
• Segment customers based on attributes such as demographics or purchase history
• Use clustering algorithms (e.g. K-Means, Hierarchical Clustering) for grouping
• Interactive dashboard to explore segment insights and patterns
• Built with Python, pandas, scikit-learn, and visualization tools like Matplotlib or Plotly
🔗 Explore the full project here:
Customer Segmentation Dashboard – View Project
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Visualize and analyze customer groups using machine learning. This project is perfect for developers building segmentation tools and marketing analytics dashboards.
Key Features
• Segment customers based on attributes such as demographics or purchase history
• Use clustering algorithms (e.g. K-Means, Hierarchical Clustering) for grouping
• Interactive dashboard to explore segment insights and patterns
• Built with Python, pandas, scikit-learn, and visualization tools like Matplotlib or Plotly
🔗 Explore the full project here:
Customer Segmentation Dashboard – View Project
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❤1
Update Gadh
Best Employee Performance Prediction System Using Machine Learning
The Employee Performance Prediction System is a complete web application developed using Python, Flask, and machine learning libraries.
📈 Employee Performance Prediction – Python/ML Project
Anticipate employee performance using historical data and machine learning. Ideal for developers building tools for HR analytics and workforce management.
Key Features
• Predicts performance ratings based on features like attendance, tasks completed, and reviews
• Utilizes models such as Random Forest, Logistic Regression, or XGBoost
• Visualizes actual vs. predicted performance trends
• Built using Python libraries like pandas, scikit-learn, and visualization tools (Matplotlib/Seaborn)
🔗 Explore the full project here:
Employee Performance Prediction – View Project
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Anticipate employee performance using historical data and machine learning. Ideal for developers building tools for HR analytics and workforce management.
Key Features
• Predicts performance ratings based on features like attendance, tasks completed, and reviews
• Utilizes models such as Random Forest, Logistic Regression, or XGBoost
• Visualizes actual vs. predicted performance trends
• Built using Python libraries like pandas, scikit-learn, and visualization tools (Matplotlib/Seaborn)
🔗 Explore the full project here:
Employee Performance Prediction – View Project
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Website https://updategadh.com
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New project
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💼 Salary Prediction System – Python/ML Project
Forecast employee salaries accurately using historical data and machine learning. Ideal for developers building data-driven tools for HR analytics and compensation planning.
Key Features
• Predict salary based on features like experience, education, skills, and location
• Utilizes models such as Linear Regression, Random Forest, or XGBoost
• Visualizes predicted vs actual salary trends with charts
• Built with Python libraries like pandas, scikit-learn, and data visualization tools
🔗 Explore the full project here:
Salary Prediction System – View Project
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Forecast employee salaries accurately using historical data and machine learning. Ideal for developers building data-driven tools for HR analytics and compensation planning.
Key Features
• Predict salary based on features like experience, education, skills, and location
• Utilizes models such as Linear Regression, Random Forest, or XGBoost
• Visualizes predicted vs actual salary trends with charts
• Built with Python libraries like pandas, scikit-learn, and data visualization tools
🔗 Explore the full project here:
Salary Prediction System – View Project
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❤1
💼 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:
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ProjectWithSourceCodes
Projects with source code | Android | Java | LLM | Python | Ai | JSP | Php |
Website https://updategadh.com
Admin https://t.me/Rishabhsaini0204
New project
https://www.youtube.com/c/decodeit2
Website https://updategadh.com
Admin https://t.me/Rishabhsaini0204
New project
https://www.youtube.com/c/decodeit2
❤1
💼 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/
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#EmployeeSalaryPrediction #PythonProject #MachineLearning #DataScience #HRAnalytics #RegressionModel #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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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
#EmployeeSalaryPrediction #PythonProject #MachineLearning #DataScience #HRAnalytics #RegressionModel #OpenSourceCode #FinalYearProject #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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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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🤝 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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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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#Python #MachineLearning #Flask #XGBoost #BikePricePrediction #DataScience #WebApp #MLProject #SourceCode #ProjectShowcase
🤝 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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https://t.me/Projectwithsourcecodes
#Python #MachineLearning #Flask #XGBoost #BikePricePrediction #DataScience #WebApp #MLProject #SourceCode #ProjectShowcase
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
#Python #MachineLearning #Streamlit #RecommendationSystem #GameRecommender #DataScience #AI #MLProject #SourceCode #ProjectShowcase
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
#Python #MachineLearning #Streamlit #RecommendationSystem #GameRecommender #DataScience #AI #MLProject #SourceCode #ProjectShowcase
🤝 Oral Cancer Detection – Python / Deep Learning
A deep-learning project that analyses oral cavity images to detect and localise potential cancerous lesions using CNN architectures. It helps automate early screening by classifying images (benign vs malignant) and providing segmentation/localisation to assist clinicians and researchers.
Key Features
• Image preprocessing: ROI extraction, augmentation, normalization
• CNN classification (e.g., ResNet / EfficientNet) for malignancy prediction
• Segmentation module (U-Net style) to highlight affected regions
• Model evaluation with accuracy, precision, recall & confusion matrix
• Optional web interface for image upload and instant prediction
🔗 Explore the full project here:
Oral Cancer Detection – View Project
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https://t.me/Projectwithsourcecodes
#Python #DeepLearning #ComputerVision #OralCancer #HealthcareAI #MedicalImaging #CNN #UNet #MLProject #DataScience #AIForGood #ProjectShowcase
A deep-learning project that analyses oral cavity images to detect and localise potential cancerous lesions using CNN architectures. It helps automate early screening by classifying images (benign vs malignant) and providing segmentation/localisation to assist clinicians and researchers.
Key Features
• Image preprocessing: ROI extraction, augmentation, normalization
• CNN classification (e.g., ResNet / EfficientNet) for malignancy prediction
• Segmentation module (U-Net style) to highlight affected regions
• Model evaluation with accuracy, precision, recall & confusion matrix
• Optional web interface for image upload and instant prediction
🔗 Explore the full project here:
Oral Cancer Detection – View Project
📢 Discover more projects:
https://t.me/Projectwithsourcecodes
#Python #DeepLearning #ComputerVision #OralCancer #HealthcareAI #MedicalImaging #CNN #UNet #MLProject #DataScience #AIForGood #ProjectShowcase