ππ¦ Food Delivery Time Prediction β Data Science Project π§
Predict delivery time based on restaurant, order, and traffic data using powerful machine learning techniques!
π Project Highlights:
β Predict food delivery ETA using ML algorithms
π Involves feature engineering, regression models
π Built with Python, Pandas, scikit-learn
π Ideal for Data Science and Final Year Projects
π Download Now:
π Food Delivery Time Prediction β Project
π’ Explore more ML & Data Science projects:
π https://t.me/Projectwithsourcecodes
π¦ Make your ML skills deliver on time!
#FoodDeliveryPrediction #DataScienceProject #MachineLearning #MLProject #PythonProjects #ETAprediction #FinalYearProject #OpenSourceCode #projectwithsourcecodes #DeliveryAnalytics
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Predict delivery time based on restaurant, order, and traffic data using powerful machine learning techniques!
π Project Highlights:
β Predict food delivery ETA using ML algorithms
π Involves feature engineering, regression models
π Built with Python, Pandas, scikit-learn
π Ideal for Data Science and Final Year Projects
π Download Now:
π Food Delivery Time Prediction β Project
π’ Explore more ML & Data Science projects:
π https://t.me/Projectwithsourcecodes
π¦ Make your ML skills deliver on time!
#FoodDeliveryPrediction #DataScienceProject #MachineLearning #MLProject #PythonProjects #ETAprediction #FinalYearProject #OpenSourceCode #projectwithsourcecodes #DeliveryAnalytics
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β€3
https://updategadh.com/
Face Recognition Based Attendance Management System β A Complete Python Project
The Face Recognition Attendance System is a desktop app made with Python and OpenCV. It basically takes attendance by recognizing faces
π§βπ«πΈ Face Recognition Based Attendance System β Python Project π§
Automate classroom or workplace attendance using real-time face recognition technology powered by OpenCV and Python!
π Key Features:
β Real-time face detection and recognition
π Auto-mark attendance with timestamps
π Student/employee database management
π§ Uses OpenCV, Haar Cascades & SQLite
π₯οΈ GUI-based interface (Tkinter)
π Get the Full Project:
π Face Recognition Attendance β Python
π’ Discover more AI & automation projects:
π https://t.me/Projectwithsourcecodes
#FaceRecognition #AttendanceSystem #PythonProject #OpenCV #AIinEducation #Automation #MLProject #Tkinter #PaidProject #projectwithsourcecodes #SmartAttendance
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Automate classroom or workplace attendance using real-time face recognition technology powered by OpenCV and Python!
π Key Features:
β Real-time face detection and recognition
π Auto-mark attendance with timestamps
π Student/employee database management
π§ Uses OpenCV, Haar Cascades & SQLite
π₯οΈ GUI-based interface (Tkinter)
π Get the Full Project:
π Face Recognition Attendance β Python
π’ Discover more AI & automation projects:
π https://t.me/Projectwithsourcecodes
#FaceRecognition #AttendanceSystem #PythonProject #OpenCV #AIinEducation #Automation #MLProject #Tkinter #PaidProject #projectwithsourcecodes #SmartAttendance
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π Life Expectancy Prediction β Data Science Project
A powerful predictive model that estimates life expectancy based on various socio-economic and health-related factors. This project is ideal for those exploring data-driven healthcare and policy analytics.
π§ Key Highlights
β’ Predicts life expectancy using regression models
β’ Analyzes features like GDP, BMI, immunization rates, etc.
β’ Data preprocessing, EDA & model evaluation included
β’ Developed using Python, Pandas, Matplotlib, Sklearn
β’ Great for data science portfolios and academic use
π Explore Full Project:
Life Expectancy Prediction β View Now
π’ Find more real-world data science projects:
π https://t.me/Projectwithsourcecodes
#LifeExpectancyPrediction #DataScienceProject #MachineLearning #HealthAnalytics #PythonML #RegressionAnalysis #PublicHealth #projectwithsourcecodes #MLProject #Sklearn #FinalYearProject
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A powerful predictive model that estimates life expectancy based on various socio-economic and health-related factors. This project is ideal for those exploring data-driven healthcare and policy analytics.
π§ Key Highlights
β’ Predicts life expectancy using regression models
β’ Analyzes features like GDP, BMI, immunization rates, etc.
β’ Data preprocessing, EDA & model evaluation included
β’ Developed using Python, Pandas, Matplotlib, Sklearn
β’ Great for data science portfolios and academic use
π Explore Full Project:
Life Expectancy Prediction β View Now
π’ Find more real-world data science projects:
π https://t.me/Projectwithsourcecodes
#LifeExpectancyPrediction #DataScienceProject #MachineLearning #HealthAnalytics #PythonML #RegressionAnalysis #PublicHealth #projectwithsourcecodes #MLProject #Sklearn #FinalYearProject
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π§ Water Quality Prediction β Data Science Project
A machine learningβbased 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.
π¬ Project Highlights
β’ Predicts water potability using supervised ML models
β’ Involves data cleaning, visualization & feature engineering
β’ Algorithms: Logistic Regression, Random Forest, SVM, etc.
β’ Built with Python, Pandas, Scikit-learn, Matplotlib
β’ Ideal for environmental science and public safety solutions
π Explore Full Project:
Water Quality Prediction β View Now
π’ Explore more impactful data science projects:
π https://t.me/Projectwithsourcecodes
#WaterQuality #DataScienceProject #MLProject #EnvironmentalTech #PythonProject #WaterPotability #CleanWater #MachineLearning #Sustainability #projectwithsourcecodes #PublicHealthAI
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A machine learningβbased 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.
π¬ Project Highlights
β’ Predicts water potability using supervised ML models
β’ Involves data cleaning, visualization & feature engineering
β’ Algorithms: Logistic Regression, Random Forest, SVM, etc.
β’ Built with Python, Pandas, Scikit-learn, Matplotlib
β’ Ideal for environmental science and public safety solutions
π Explore Full Project:
Water Quality Prediction β View Now
π’ Explore more impactful data science projects:
π https://t.me/Projectwithsourcecodes
#WaterQuality #DataScienceProject #MLProject #EnvironmentalTech #PythonProject #WaterPotability #CleanWater #MachineLearning #Sustainability #projectwithsourcecodes #PublicHealthAI
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β€1
π College Prediction System
Predict the best-fit colleges based on your academic profile.
Built with Python, Flask, and Machine Learning.
π€ AI-based recommendation engine
π Score & rank-based predictions
π Filters by location & preferences
π Interactive results dashboard
π Project Link: College Prediction System
#Python #MachineLearning #AI #CollegePrediction #StudentLife #DataScience #Flask #TechForEducation #EdTech #CodingProjects #OpenSource #SoftwareDevelopment #ProgrammingLife #LearnToCode #DataDriven #StudentGoals #MLProject #TechCommunity #StudySmart #AIinEducation
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Predict the best-fit colleges based on your academic profile.
Built with Python, Flask, and Machine Learning.
π€ AI-based recommendation engine
π Score & rank-based predictions
π Filters by location & preferences
π Interactive results dashboard
π Project Link: College Prediction System
#Python #MachineLearning #AI #CollegePrediction #StudentLife #DataScience #Flask #TechForEducation #EdTech #CodingProjects #OpenSource #SoftwareDevelopment #ProgrammingLife #LearnToCode #DataDriven #StudentGoals #MLProject #TechCommunity #StudySmart #AIinEducation
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Update Gadh
Best College Predictorβ A Django-Based Web Application
The College Predictor is a purpose-built web application designed to help students estimate potential colleges they can secure admission
π Your AI Guide to College Admissions!
Confused about which college you might get into? The College Predictor project uses Python and Machine Learning to forecast your chances, helping you shortlist the best-fit institutions based on your entrance exam rank, category, and preferences.
β¨ Key Features:
π€ ML-Based Predictions β Accurate results using historical admission data.
π Rank & Category Filters β Tailored predictions to your profile.
π State & College Filters β Narrow down to your preferred location.
π User-Friendly Web Interface β Clean and intuitive design for easy access.
π Explore the Project: https://updategadh.com/python-projects/college-predictor/
#Python #MachineLearning #AI #CollegePrediction #StudentLife #DataScience #Flask #EdTech #CodingProjects #OpenSource #SoftwareDevelopment #ProgrammingLife #LearnToCode #DataDriven #MLProject #TechCommunity #StudySmart #AIinEducation #Admissions
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Confused about which college you might get into? The College Predictor project uses Python and Machine Learning to forecast your chances, helping you shortlist the best-fit institutions based on your entrance exam rank, category, and preferences.
β¨ Key Features:
π€ ML-Based Predictions β Accurate results using historical admission data.
π Rank & Category Filters β Tailored predictions to your profile.
π State & College Filters β Narrow down to your preferred location.
π User-Friendly Web Interface β Clean and intuitive design for easy access.
π Explore the Project: https://updategadh.com/python-projects/college-predictor/
#Python #MachineLearning #AI #CollegePrediction #StudentLife #DataScience #Flask #EdTech #CodingProjects #OpenSource #SoftwareDevelopment #ProgrammingLife #LearnToCode #DataDriven #MLProject #TechCommunity #StudySmart #AIinEducation #Admissions
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π€ Student Exam Performance Prediction β 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
β’ Data preprocessing: handle missing values, normalization, encoding categorical variables
β’ Feature selection to identify major factors affecting performance
β’ Model training: regression/classification models like Random Forest, Decision Trees, or Logistic Regression
β’ Evaluation metrics: accuracy, precision, recall, F1-score, possibly confusion matrix
β’ Prediction interface: input student details β output expected performance
π Explore the full project here:
Student Exam Performance Prediction β View Project
π’ Discover more projects:
https://t.me/Projectwithsourcecodes
#DataScience #MachineLearning #StudentPerformance #PredictiveAnalytics #MLProject #Python #Pandas #ScikitLearn #EducationData #ProjectShowcase
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
β’ Data preprocessing: handle missing values, normalization, encoding categorical variables
β’ Feature selection to identify major factors affecting performance
β’ Model training: regression/classification models like Random Forest, Decision Trees, or Logistic Regression
β’ Evaluation metrics: accuracy, precision, recall, F1-score, possibly confusion matrix
β’ Prediction interface: input student details β output expected performance
π Explore the full project here:
Student Exam Performance Prediction β View Project
π’ Discover more projects:
https://t.me/Projectwithsourcecodes
#DataScience #MachineLearning #StudentPerformance #PredictiveAnalytics #MLProject #Python #Pandas #ScikitLearn #EducationData #ProjectShowcase
ChatGPT said:
π€ Bike Price Prediction β 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
β’ Flask web interface for user input
β’ XGBoost model for price prediction
β’ Data preprocessing and feature selection
β’ High model accuracy on test data
β’ Clean and responsive result display
π Explore the full project here:
Bike Price Prediction β View Project
π’ Discover more projects:
https://t.me/Projectwithsourcecodes
#Python #MachineLearning #Flask #XGBoost #BikePricePrediction #DataScience #WebApp #MLProject #SourceCode #ProjectShowcase
π€ Bike Price Prediction β 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
β’ Flask web interface for user input
β’ XGBoost model for price prediction
β’ Data preprocessing and feature selection
β’ High model accuracy on test data
β’ Clean and responsive result display
π Explore the full project here:
Bike Price Prediction β View Project
π’ Discover more projects:
https://t.me/Projectwithsourcecodes
#Python #MachineLearning #Flask #XGBoost #BikePricePrediction #DataScience #WebApp #MLProject #SourceCode #ProjectShowcase
π€ Game Recommender System β 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
β’ Personalized game recommendations
β’ Collaborative & content-based filtering models
β’ Real dataset integration for accurate results
β’ Streamlit-based interactive user interface
β’ Modular and easy-to-customize code structure
π Explore the full project here:
Game Recommender System β View Project
π’ Discover more projects:
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
β’ Personalized game recommendations
β’ Collaborative & content-based filtering models
β’ Real dataset integration for accurate results
β’ Streamlit-based interactive user interface
β’ Modular and easy-to-customize code structure
π Explore the full project here:
Game Recommender System β View Project
π’ Discover more projects:
https://t.me/Projectwithsourcecodes
#Python #MachineLearning #Streamlit #RecommendationSystem #GameRecommender #DataScience #AI #MLProject #SourceCode #ProjectShowcase
π€ Oral Cancer Detection β 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
β’ Image preprocessing: ROI extraction, augmentation, normalization
β’ CNN classification (e.g., ResNet / EfficientNet) for malignancy prediction
β’ Segmentation module (U-Net style) to highlight affected regions
β’ Model evaluation with accuracy, precision, recall & confusion matrix
β’ Optional web interface for image upload and instant prediction
π Explore the full project here:
Oral Cancer Detection β View Project
π’ Discover more projects:
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
β’ Image preprocessing: ROI extraction, augmentation, normalization
β’ CNN classification (e.g., ResNet / EfficientNet) for malignancy prediction
β’ Segmentation module (U-Net style) to highlight affected regions
β’ Model evaluation with accuracy, precision, recall & confusion matrix
β’ Optional web interface for image upload and instant prediction
π Explore the full project here:
Oral Cancer Detection β View Project
π’ Discover more projects:
https://t.me/Projectwithsourcecodes
#Python #DeepLearning #ComputerVision #OralCancer #HealthcareAI #MedicalImaging #CNN #UNet #MLProject #DataScience #AIForGood #ProjectShowcase
Car Price Prediction System using Machine Learning (Final Year Project)
Want a trending ML project that looks professional in viva and interviews?
This project predicts used car resale prices using real Machine Learning algorithms like Random Forest, with full preprocessing and evaluation metrics.
βοΈ Real-world problem solving
βοΈ Streamlit Web App included
βοΈ Trained ML Model
βοΈ Complete Source Code
βοΈ Report + PPT + Viva Questions
βοΈ Ready-to-run ZIP
Perfect for BCA / MCA / B.Tech / M.Tech / Data Science students π
π Get full details here:
https://updategadh.com/data-science-project/car-price-prediction-system/
#MachineLearning #FinalYearProject #DataScienceProject #PythonProject #MLProject #UpdateGadh
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Stop submitting basic Machine Learning notebooks π΄
I just uploaded a complete Car Price Prediction System that looks like a real product β not just college assignment.
This project includes:
βοΈ Real Machine Learning Model
βοΈ Random Forest Regression
βοΈ Proper Data Preprocessing
βοΈ Evaluation Metrics (RΒ², MAE, RMSE)
βοΈ Working Web App UI
βοΈ Professional Folder Structure
This is the type of project that:
β Impresses External Examiner
β Strengthens Your Resume
β Helps in ML Interviews
β Makes You Stand Out
Most students just train Linear Regression and stop.
But this one?
Itβs a complete working ML system π
https://youtu.be/66Limv4yXm8
π Full Project with Code + Report:
https://updategadh.com/data-science-project/car-price-prediction-system/
Build something powerful now.
#FinalYearProject #MachineLearning #DataScience #PythonProject #MLProject #EngineeringStudents #UpdateGadh
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Car Price Prediction System using Machine Learning || Final Year ML Project with Source Code
Car Price Prediction System using Machine Learning | Final Year ML Project with Source Code
In this video, I demonstrate a complete Machine Learning project that predicts used car resale prices using Random Forest Regression and a full preprocessing pipeline.β¦
In this video, I demonstrate a complete Machine Learning project that predicts used car resale prices using Random Forest Regression and a full preprocessing pipeline.β¦
π¨ Final Year Alert! π¨
Stop wasting your final year π³
Boost your CV with a killer ML project π€
2026 me sirf ye skills chalengi π
Marketers, employers are looking for:
1οΈβ£ AI-driven insights
2οΈβ£ Predictive modeling
3οΈβ£ Data science expertise
90% students donβt know this π
Don't be a statistic! Learn from the experts at updategadh.com π
Get ahead of the curve with our:
πΉ Hands-on ML project guide
πΉ Interactive tutorials & exercises
πΉ Real-world case studies
#MLProject #FinalYearStudents #CareerBoost
Stop wasting your final year π³
Boost your CV with a killer ML project π€
2026 me sirf ye skills chalengi π
Marketers, employers are looking for:
1οΈβ£ AI-driven insights
2οΈβ£ Predictive modeling
3οΈβ£ Data science expertise
90% students donβt know this π
Don't be a statistic! Learn from the experts at updategadh.com π
Get ahead of the curve with our:
πΉ Hands-on ML project guide
πΉ Interactive tutorials & exercises
πΉ Real-world case studies
#MLProject #FinalYearStudents #CareerBoost
π₯ STOP! Is your College ML Project just... 'meh'? π€―
You've trained the model, got decent accuracy... but in the real world? It crashes or gives weird results. π© The secret isn't just fancy algorithms, it's about giving your model CLEAN, USABLE data. Think of it as feeding a gourmet meal to a super AI β garbage in, garbage out! ποΈβ‘οΈπ€
This is why Data Preprocessing is your superpower! πͺ Scaling your features helps your model learn better and faster, preventing headaches later.
Hereβs a sneak peek at scaling with
Pro Tip: Always scale your data BEFORE feeding it to most ML models, especially those using distance calculations (like K-Means, SVMs, or Gradient Descent-based models). It's an interview favorite! π
---
β Quick Question:
Which of these is NOT typically a data preprocessing step in Machine Learning?
a) Feature Scaling
b) Handling Missing Values
c) Model Deployment
d) One-Hot Encoding
Leave your answer in the comments! π
---
Want more such practical tips & project ideas?
Join https://t.me/Projectwithsourcecodes.
#AI #MachineLearning #Python #Coding #DataScience #MLProject #CollegeLife #TechTips #CodingStudents #ProjectIdeas
You've trained the model, got decent accuracy... but in the real world? It crashes or gives weird results. π© The secret isn't just fancy algorithms, it's about giving your model CLEAN, USABLE data. Think of it as feeding a gourmet meal to a super AI β garbage in, garbage out! ποΈβ‘οΈπ€
This is why Data Preprocessing is your superpower! πͺ Scaling your features helps your model learn better and faster, preventing headaches later.
Hereβs a sneak peek at scaling with
StandardScaler in Python:# Supercharge your data! π
from sklearn.preprocessing import StandardScaler
import numpy as np
# Imagine this is your raw, messy project data
# Features like age, income, and score (very different scales!)
X_train_raw = np.array([
[25, 50000, 85],
[50, 150000, 92],
[20, 30000, 78]
])
# Create the scaler object
scaler = StandardScaler()
# Fit & Transform: This makes your data 'normal'
# All features will have a mean of 0 and std dev of 1
X_train_scaled = scaler.fit_transform(X_train_raw)
print("Original Data (Messy):\n", X_train_raw)
print("\nScaled Data (Ready for Action!):\n", X_train_scaled)
Pro Tip: Always scale your data BEFORE feeding it to most ML models, especially those using distance calculations (like K-Means, SVMs, or Gradient Descent-based models). It's an interview favorite! π
---
β Quick Question:
Which of these is NOT typically a data preprocessing step in Machine Learning?
a) Feature Scaling
b) Handling Missing Values
c) Model Deployment
d) One-Hot Encoding
Leave your answer in the comments! π
---
Want more such practical tips & project ideas?
Join https://t.me/Projectwithsourcecodes.
#AI #MachineLearning #Python #Coding #DataScience #MLProject #CollegeLife #TechTips #CodingStudents #ProjectIdeas
β€1
β‘οΈ AI Smart Energy Consumption Analyzer
π Final Year Project 2025 | Free Download
Predict your home's energy usage BEFORE it spikes β powered by
XGBoost Machine Learning + Flask Web App!
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π₯ WHAT'S INSIDE?
ββββββββββββββββββββββββ
β XGBoost AI Model β ~94% prediction accuracy
β Live Dashboard β Real-time kWh meter & stats
β Bill Estimator β Hourly / Daily / Monthly cost (βΉ)
β AI Energy Tips β Smart saving recommendations
β 4 Analytics Charts β Heatmap, Trend, Bar, Profile
β REST API β Auto-refreshes every 5 seconds
β Login System β Admin & Student roles
β Dark UI β Fully responsive & modern design
β One-Click Launch β python run.py and done!
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π TECH STACK
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π Python 3 | π€ XGBoost | π Flask
πΌ Pandas & NumPy | π¨ Matplotlib & Seaborn
πΎ Joblib | π₯ HTML / CSS / JavaScript
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π LOGIN CREDENTIALS
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π€ Admin β admin / admin123
π Student β student / student123
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βΆοΈ HOW TO RUN (3 Steps)
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1οΈβ£ pip install flask xgboost pandas numpy
matplotlib seaborn scikit-learn joblib
2οΈβ£ python run.py
3οΈβ£ Open β http://127.0.0.1:5000 π
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π₯ FREE DOWNLOAD
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π Full Tutorial β https://updategadh.com/ai-based-smart-energy-consumption/
π Source Code β https://t.me/Projectwithsourcecodes/1603
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π¬ Drop a comment if you found this helpful!
π Like & Share with your classmates
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#XGBoost #Flask #EnergyAnalyzer #AIProject
#PythonFlask #DataScience #WebDevelopment
#FreeSourceCode #MLProject #Updategadh
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π Final Year Project 2025 | Free Download
Predict your home's energy usage BEFORE it spikes β powered by
XGBoost Machine Learning + Flask Web App!
ββββββββββββββββββββββββ
π₯ WHAT'S INSIDE?
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β XGBoost AI Model β ~94% prediction accuracy
β Live Dashboard β Real-time kWh meter & stats
β Bill Estimator β Hourly / Daily / Monthly cost (βΉ)
β AI Energy Tips β Smart saving recommendations
β 4 Analytics Charts β Heatmap, Trend, Bar, Profile
β REST API β Auto-refreshes every 5 seconds
β Login System β Admin & Student roles
β Dark UI β Fully responsive & modern design
β One-Click Launch β python run.py and done!
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π TECH STACK
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π Python 3 | π€ XGBoost | π Flask
πΌ Pandas & NumPy | π¨ Matplotlib & Seaborn
πΎ Joblib | π₯ HTML / CSS / JavaScript
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π LOGIN CREDENTIALS
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π€ Admin β admin / admin123
π Student β student / student123
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βΆοΈ HOW TO RUN (3 Steps)
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1οΈβ£ pip install flask xgboost pandas numpy
matplotlib seaborn scikit-learn joblib
2οΈβ£ python run.py
3οΈβ£ Open β http://127.0.0.1:5000 π
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π₯ FREE DOWNLOAD
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π Full Tutorial β https://updategadh.com/ai-based-smart-energy-consumption/
π Source Code β https://t.me/Projectwithsourcecodes/1603
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π¬ Drop a comment if you found this helpful!
π Like & Share with your classmates
#FinalYearProject #PythonProject #MachineLearning
#XGBoost #Flask #EnergyAnalyzer #AIProject
#PythonFlask #DataScience #WebDevelopment
#FreeSourceCode #MLProject #Updategadh
#FYP2025 #PythonTutorial #DeepLearning
#SmartEnergy #IoTProject #AIforGood
https://updategadh.com/
AI-Based Smart Energy Consumption Analyzer and Optimization
The AI-Based Smart Energy Consumption Analyzer is an intelligent .Are you looking for a final year project on Artificial Intelligence and Machine