π Crime Rate Predictor β Python / Machine Learning Project
Empower public safety with data-driven foresight. This Crime Rate Predictor uses Python and a Random Forest Regression model to forecast crime rates across 19 major Indian cities. Ideal for developers exploring predictive analytics, smart cities, and real-world data science impact.
Key Features
β’ Predicts rates for 10 different crime categories (e.g., cybercrimes, crimes against women)
β’ Covers 19 Indian metropolitan cities with data spanning 2014 to 2021
GitHub
+1
β’ Built using Random Forest Regression for high accuracy (~93.2%)
GitHub
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β’ User-friendly interface: select city, crime type, and yearβget immediate predictions
π Explore the full project here:
Crime Rate Predictor β View Project
π’ Discover more practical, real-world Python and ML projects:
https://t.me/Projectwithsourcecodes
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Key Features
β’ Predicts rates for 10 different crime categories (e.g., cybercrimes, crimes against women)
β’ Covers 19 Indian metropolitan cities with data spanning 2014 to 2021
GitHub
+1
β’ Built using Random Forest Regression for high accuracy (~93.2%)
GitHub
+1
β’ User-friendly interface: select city, crime type, and yearβget immediate predictions
π Explore the full project here:
Crime Rate Predictor β View Project
π’ Discover more practical, real-world Python and ML projects:
https://t.me/Projectwithsourcecodes
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Best Language Detection Web App using Machine Learning & NLP
Language Detection is an advanced machine learning application built to accurately identify and classify the language of any given text.
π Language Detection System β Python Project
Identify the language of any text instantly using Python. A great project for exploring NLP and building multilingual-ready applications.
Key Features
β’ Detects language from text input with high accuracy
β’ Supports 50+ global languages
β’ Uses Python NLP libraries for processing
β’ Easy-to-understand, modular code suitable for portfolios
π Explore the full project here:
Language Detection System β View Project
π’ Discover more practical Python & ML projects:
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Identify the language of any text instantly using Python. A great project for exploring NLP and building multilingual-ready applications.
Key Features
β’ Detects language from text input with high accuracy
β’ Supports 50+ global languages
β’ Uses Python NLP libraries for processing
β’ Easy-to-understand, modular code suitable for portfolios
π Explore the full project here:
Language Detection System β View Project
π’ Discover more practical Python & ML projects:
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Stock Price Prediction with Machine Learning β Professional Django Web App
Stock Price Prediction with Machine Learning is a fully functional web application that provides real-time stock market insights along with accurate
π Stock Price Prediction β Python Project
Predict future stock market trends using Python and machine learning. A perfect project for finance, data science, and AI enthusiasts looking to explore predictive analytics.
Key Features
β’ Real-time stock data fetching and analysis
β’ Interactive charts for trend visualization
β’ Predictive model using historical data
β’ Simple, user-friendly interface for quick insights
π Explore the full project here:
Stock Price Prediction β View Project
π’ Discover more practical Python & ML projects:
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Predict future stock market trends using Python and machine learning. A perfect project for finance, data science, and AI enthusiasts looking to explore predictive analytics.
Key Features
β’ Real-time stock data fetching and analysis
β’ Interactive charts for trend visualization
β’ Predictive model using historical data
β’ Simple, user-friendly interface for quick insights
π Explore the full project here:
Stock Price Prediction β View Project
π’ Discover more practical Python & ML projects:
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π° Gold Price Prediction β Python Project
Predict gold price trends using data science and machine learning. Ideal for developers looking to apply forecasting techniques in finance.
Key Features
β’ Uses historical gold pricing data for modeling
β’ Employs regression techniques (e.g., Linear Regression, ARIMA)
β’ Visualizes predicted vs actual price trends
β’ Built with Python libraries like pandas, scikit-learn, or statsmodels
π Explore the full project here:
Gold Price Prediction β View Project
π’ Discover more practical, real-world Python & ML projects:
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Predict gold price trends using data science and machine learning. Ideal for developers looking to apply forecasting techniques in finance.
Key Features
β’ Uses historical gold pricing data for modeling
β’ Employs regression techniques (e.g., Linear Regression, ARIMA)
β’ Visualizes predicted vs actual price trends
β’ Built with Python libraries like pandas, scikit-learn, or statsmodels
π Explore the full project here:
Gold Price Prediction β View Project
π’ Discover more practical, real-world Python & ML projects:
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π Hotel Price Prediction β Python Project
Predict hotel room prices using machine learning and historical data. Ideal for developers exploring regression, time-series models, and pricing strategy tools.
Key Features
β’ Uses historical hotel data to train prediction models
β’ Applies regression techniques (e.g., Linear Regression, Random Forest, XGBoost)
β’ Visual comparison of actual vs. predicted price trends
β’ Built with Python data libraries like pandas, scikit-learn, or statsmodels
π Explore the full project here:
Hotel Price Prediction β View Project
π’ Discover more practical Python & ML projects:
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Predict hotel room prices using machine learning and historical data. Ideal for developers exploring regression, time-series models, and pricing strategy tools.
Key Features
β’ Uses historical hotel data to train prediction models
β’ Applies regression techniques (e.g., Linear Regression, Random Forest, XGBoost)
β’ Visual comparison of actual vs. predicted price trends
β’ Built with Python data libraries like pandas, scikit-learn, or statsmodels
π Explore the full project here:
Hotel Price Prediction β View Project
π’ Discover more practical Python & ML projects:
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π² Bike Sharing Demand Prediction β Python Project
Forecast how many bikes will be rented using historical data and predictive analytics. Ideal for those working on urban mobility, time-series forecasting, or ML in smart cities.
Key Features
β’ Uses historical bike-sharing data with features like weather, hour, day, season, and holidays
β’ Applies regression models such as Linear Regression, Random Forest, Gradient Boosting, or XGBoost
β’ Visualizes actual vs. predicted demand trends
β’ Built with Python data science libraries like pandas, scikit-learn, and possibly Streamlit or Flask for interface
π Explore the full project here:
Bike Sharing Demand Prediction β View Project
π’ Discover more practical Python & ML projects:
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Forecast how many bikes will be rented using historical data and predictive analytics. Ideal for those working on urban mobility, time-series forecasting, or ML in smart cities.
Key Features
β’ Uses historical bike-sharing data with features like weather, hour, day, season, and holidays
β’ Applies regression models such as Linear Regression, Random Forest, Gradient Boosting, or XGBoost
β’ Visualizes actual vs. predicted demand trends
β’ Built with Python data science libraries like pandas, scikit-learn, and possibly Streamlit or Flask for interface
π Explore the full project here:
Bike Sharing Demand Prediction β View Project
π’ Discover more practical Python & ML projects:
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π Employee Attrition Prediction β Python/ML Project
Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
β’ Trains models on employee demographics, performance, and engagement metrics
β’ Predicts attrition risk using algorithms like Logistic Regression or Random Forest
β’ Provides visual dashboards comparing predicted vs actual turnover trends
β’ Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
π Explore the full project here:
Employee Attrition Prediction β View Project
π’ Discover more practical Python & ML projects:
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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
β’ Trains models on employee demographics, performance, and engagement metrics
β’ Predicts attrition risk using algorithms like Logistic Regression or Random Forest
β’ Provides visual dashboards comparing predicted vs actual turnover trends
β’ Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
π Explore the full project here:
Employee Attrition Prediction β View Project
π’ Discover more practical Python & ML projects:
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π Employee Attrition Prediction β Python/ML Project
Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
β’ Trains models on employee demographics, performance, and engagement metrics
β’ Predicts attrition risk using algorithms like Logistic Regression or Random Forest
β’ Provides visual dashboards comparing predicted vs actual turnover trends
β’ Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
π Explore the full project here:
Employee Attrition Prediction β View Project
π’ Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
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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
β’ Trains models on employee demographics, performance, and engagement metrics
β’ Predicts attrition risk using algorithms like Logistic Regression or Random Forest
β’ Provides visual dashboards comparing predicted vs actual turnover trends
β’ Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
π Explore the full project here:
Employee Attrition Prediction β View Project
π’ Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
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π Hotel Booking Cancellation Prediction β 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
β’ Analyzes guest profiles, booking channel, stay dates, and special requests
β’ Uses algorithms like Logistic Regression, Random Forest, or Gradient Boosting
β’ Visualizes actual vs predicted cancellation trends for clear insight
β’ Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
π Explore the full project here:
Hotel Booking Cancellation Prediction β View Project
π’ Discover more practical Python & ML projects:
https://t.me/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
β’ Analyzes guest profiles, booking channel, stay dates, and special requests
β’ Uses algorithms like Logistic Regression, Random Forest, or Gradient Boosting
β’ Visualizes actual vs predicted cancellation trends for clear insight
β’ Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
π Explore the full project here:
Hotel Booking Cancellation Prediction β View Project
π’ Discover more practical Python & ML projects:
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π Marketing Campaign Demand Prediction β 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
β’ Analyzes customer demographics, campaign types, and prior purchasing behavior
β’ Uses regression or classification models like Random Forest, XGBoost, or Logistic Regression
β’ Visualizes prediction performance with charts comparing actual and forecasted demand
β’ Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
π Explore the full project here:
Marketing Campaign Demand Prediction β View Project
π’ Discover more practical Python & ML projects:
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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.
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β’ Analyzes customer demographics, campaign types, and prior purchasing behavior
β’ Uses regression or classification models like Random Forest, XGBoost, or Logistic Regression
β’ Visualizes prediction performance with charts comparing actual and forecasted demand
β’ Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
π Explore the full project here:
Marketing Campaign Demand Prediction β View Project
π’ Discover more practical Python & ML projects:
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π Customer Personality Analysis β 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
β’ Analyzes survey or transaction data to infer personality traits
β’ Applies clustering or classification models (e.g., K-Means, Decision Tree)
β’ Visualizes customer segments and trait distributions
β’ Built with Python libraries like pandas, scikit-learn, and Matplotlib/Seaborn
π Explore the full project here:
Customer Personality Analysis β View Project
π’ Discover more practical Python & ML projects:
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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
β’ Analyzes survey or transaction data to infer personality traits
β’ Applies clustering or classification models (e.g., K-Means, Decision Tree)
β’ Visualizes customer segments and trait distributions
β’ Built with Python libraries like pandas, scikit-learn, and Matplotlib/Seaborn
π Explore the full project here:
Customer Personality Analysis β View Project
π’ Discover more practical Python & ML projects:
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π§ͺ AI-Powered Fake News Detection β 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
β’ Processes news text to detect authenticityβreal vs fake
β’ Uses NLP techniques like TF-IDF, Word Embeddings, or BERT
β’ Classification models like Logistic Regression, SVM, or deep learning
β’ Visualizes prediction confidence and accuracy metrics
β’ Built with Python libraries: pandas, scikit-learn, NLTK/spacy, and possibly TensorFlow/Keras
π Explore the full project here:
AI-Powered Fake News Detection β View Project
π’ Discover more practical Python & ML projects:
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Key Features
β’ Processes news text to detect authenticityβreal vs fake
β’ Uses NLP techniques like TF-IDF, Word Embeddings, or BERT
β’ Classification models like Logistic Regression, SVM, or deep learning
β’ Visualizes prediction confidence and accuracy metrics
β’ Built with Python libraries: pandas, scikit-learn, NLTK/spacy, and possibly TensorFlow/Keras
π Explore the full project here:
AI-Powered Fake News Detection β View Project
π’ Discover more practical Python & ML projects:
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π Customer Segmentation Dashboard β 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
β’ Segment customers based on attributes such as demographics or purchase history
β’ Use clustering algorithms (e.g. K-Means, Hierarchical Clustering) for grouping
β’ Interactive dashboard to explore segment insights and patterns
β’ Built with Python, pandas, scikit-learn, and visualization tools like Matplotlib or Plotly
π Explore the full project here:
Customer Segmentation Dashboard β View Project
π’ Discover more practical Python & ML projects:
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Key Features
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β’ Use clustering algorithms (e.g. K-Means, Hierarchical Clustering) for grouping
β’ Interactive dashboard to explore segment insights and patterns
β’ Built with Python, pandas, scikit-learn, and visualization tools like Matplotlib or Plotly
π Explore the full project here:
Customer Segmentation Dashboard β View Project
π’ Discover more practical Python & ML projects:
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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.
π Employee Performance Prediction β 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
β’ Predicts performance ratings based on features like attendance, tasks completed, and reviews
β’ Utilizes models such as Random Forest, Logistic Regression, or XGBoost
β’ Visualizes actual vs. predicted performance trends
β’ Built using Python libraries like pandas, scikit-learn, and visualization tools (Matplotlib/Seaborn)
π Explore the full project here:
Employee Performance Prediction β View Project
π’ Discover more practical Python & ML projects:
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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
β’ Predicts performance ratings based on features like attendance, tasks completed, and reviews
β’ Utilizes models such as Random Forest, Logistic Regression, or XGBoost
β’ Visualizes actual vs. predicted performance trends
β’ Built using Python libraries like pandas, scikit-learn, and visualization tools (Matplotlib/Seaborn)
π Explore the full project here:
Employee Performance Prediction β View Project
π’ Discover more practical Python & ML projects:
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