Skip to content
geeksforgeeks
  • Tutorials
    • Python
    • Java
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
    • Practice Coding Problems
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • Open CV
  • scikit-image
  • pycairo
  • Pyglet
  • Python
  • Numpy
  • Pandas
  • Python Database
  • Data Analysis
  • ML Math
  • Machine Learning
  • NLP
  • Deep Learning
  • Deep Learning Interview Questions
  • ML Projects
  • ML Interview Questions
  • 100 Days of Machine Learning
Open In App
Next Article:
Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
Next article icon

Age Detection using Deep Learning in OpenCV

Last Updated : 13 Apr, 2025
Comments
Improve
Suggest changes
Like Article
Like
Report

The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'll build a program that predicts the age of a person from an image using deep learning i.e Age Predictor project using OpenCV. 

Workflow of Age Prediction Model

Instead of predicting an exact age a more effective approach is to categorize age into specific ranges. Researchers found that this classification approach significantly improved accuracy in real-world scenarios. The workflow for building the age detection system includes:

Workflow with OpenCV
  1. Live Feed: The process begins with a live feed typically a video stream from a camera or webcam. This feed will be processed frame by frame to detect faces in real-time.
  2. Face Detection Model: The live feed is passed through a Face Detection Model. The model detects faces within the frame, using a deep learning-based approach like OpenCV's DNN module.
  3. Crop Face Blob from Frame: Once faces are detected the next step is to crop the face region from the detected frame. This region of interest (ROI) is then passed into the next stages for age prediction.
  4. Age Detection Model: The cropped face is passed into the Age Detection Model. This model predicts the age of the person by classifying them into specific age ranges. The model uses pre-trained networks to make predictions based on the face's features.
  5. Gender Detection Model: Similar to the age detection the cropped face is also passed into the Gender Detection Model. This model predicts the gender like male or female based on their facial characteristics.
  6. Output Image: Finally the predicted age and gender are added as text annotations on the frame. The processed frame with these annotations is shown as the output image.

Now that we know the workflow we can start budling model in python.

Implementing Age prediction Model

For simplicity and better understanding we will be only predicting age of person. Here is the step by step process to implement it.

1. Importing Libraries

First we need to import numpy and OpenCV libraries for the project. 

Python 
import cv2 import numpy as np from google.colab.patches import cv2_imshow

2. Loading Pre-trained Models

Next we'll load the pre-trained models for face detection and age prediction. OpenCV's deep neural network (DNN) module allows us to use these models. You can download all Pre trained model from here.

  • face_proto and face_model: Files for the pre-trained face detection model.
  • age_proto and age_model: Files for the pre-trained age prediction model.
  • cv2.dnn.readNetFromTensorflow: Reads the face detection model into OpenCV.
  • cv2.dnn.readNetFromCaffe: Reads the age prediction model into OpenCV.
Python 
face_proto = "opencv_face_detector.pbtxt" face_model = "opencv_face_detector_uint8.pb" age_proto = "age_deploy.prototxt" age_model = "age_net.caffemodel"  face_net = cv2.dnn.readNetFromTensorflow(face_model, face_proto) age_net = cv2.dnn.readNetFromCaffe(age_proto, age_model)

3. Detecting Faces in an Image

We now define a function to detect faces in an image. This function uses OpenCV's DNN module to process the image and find faces.

  • cv2.dnn.blobFromImage: Converts the image into a blob format required by the DNN model.
  • net.setInput(blob): Sets the input to the face detection model.
  • net.forward(): Runs the forward pass to detect faces.
  • confidence threshold: Filters out faces that have a low detection confidence.
Python 
def detect_faces(net, frame, conf_threshold=0.7):     frame_height = frame.shape[0]     frame_width = frame.shape[1]     blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123], False, False)     net.setInput(blob)     detections = net.forward()     face_boxes = []     for i in range(detections.shape[2]):         confidence = detections[0, 0, i, 2]         if confidence > conf_threshold:             x1 = int(detections[0, 0, i, 3] * frame_width)             y1 = int(detections[0, 0, i, 4] * frame_height)             x2 = int(detections[0, 0, i, 5] * frame_width)             y2 = int(detections[0, 0, i, 6] * frame_height)             face_boxes.append([x1, y1, x2, y2])             cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), int(round(frame_height/150)), 8)     return frame, face_boxes

4. Predicting Age

Once faces are detected we can predict the age for each detected face. This is done by preprocessing the face image and passing it through the age prediction model.

  • cv2.dnn.blobFromImage: Prepares the face image by resizing and normalizing it for input into the age prediction model.
  • net.forward(): Runs the forward pass to predict the age.
  • age_preds[0].argmax(): Finds the index of the highest probability, which corresponds to the predicted age range.
Python 
def predict_age(face, net):     blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)     net.setInput(blob)     age_preds = net.forward()     age = age_list[age_preds[0].argmax()]     return age

5. Processing a Single Image

Finally we will define a function to process an image. This function will load the image, detect faces, predict the age for each face and display the result.

  • cv2.imread: Reads the image from the provided path.
  • detect_faces: Detects faces in the image.
  • predict_age: Predicts the age for each face.
  • cv2.putText: Adds the predicted age as text on the image.
  • cv2_imshow: Displays the image.
Python 
def process_image(image_path):     frame = cv2.imread(image_path)       if frame is None:         print(f"Error: Image not found at {image_path}")         return      frame, face_boxes = detect_faces(face_net, frame)          for (x1, y1, x2, y2) in face_boxes:         face = frame[max(0, y1-20):min(y2+20, frame.shape[0]-1),                       max(0, x1-20):min(x2+20, frame.shape[1]-1)]         age = predict_age(face, age_net)         cv2.putText(frame, f"Age: {age}", (x1, y1-10),                     cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)       cv2_imshow(frame)     cv2.waitKey(0)     cv2.destroyAllWindows()  image_path = "kid1.jpg" process_image(image_path)

Output:

download
Age Prediction

Here used a image of kid to predict his age and we can see model is working fine by giving a age range of 4-6 for the kid.

You can easily adapt this approach to process real-time video feeds and further enhance the system by adding features like gender classification or emotion detection. Additionally experimenting with custom pre-trained models such as YOLO for face detection can improve accuracy and performance. In this article you not only learn how to integrate OpenCV with deep learning models for image processing and age detection but also gain insights into expanding the system to create more interactive and advanced applications.

Get the complete source code link here:

Source code : click here.


Next Article
Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV

B
bobde_yagyesh
Improve
Article Tags :
  • Machine Learning
  • Geeks Premier League
  • Deep Learning
  • AI-ML-DS
  • Geeks-Premier-League-2022
  • OpenCV
  • python
  • Python-OpenCV
  • Deep Learning Projects
  • AI-ML-DS With Python
Practice Tags :
  • Machine Learning
  • python

Similar Reads

    100+ Machine Learning Projects with Source Code [2025]
    This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an
    5 min read

    Classification Projects

    Wine Quality Prediction - Machine Learning
    Here we will predict the quality of wine on the basis of given features. We use the wine quality dataset available on Internet for free. This dataset has the fundamental features which are responsible for affecting the quality of the wine. By the use of several Machine learning models, we will predi
    5 min read
    Credit Card Fraud Detection - ML
    The goal of this project is to develop a machine learning model that can accurately detect fraudulent credit card transactions using historical data. By analyzing transaction patterns, the model should be able to distinguish between normal and fraudulent activity, helping financial institutions flag
    6 min read
    Disease Prediction Using Machine Learning
    Disease prediction using machine learning is used in healthcare to provide accurate and early diagnosis based on patient symptoms. We can build predictive models that identify diseases efficiently. In this article, we will explore the end-to-end implementation of such a system. Step 1: Import Librar
    5 min read
    Recommendation System in Python
    Industry leaders like Netflix, Amazon and Uber Eats have transformed how individuals access products and services. They do this by using recommendation algorithms that improve the user experience. These systems offer personalized recommendations based on users interests and preferences. In this arti
    6 min read
    Detecting Spam Emails Using Tensorflow in Python
    Spam messages are unsolicited or unwanted emails/messages sent in bulk to users. Detecting spam emails automatically helps prevent unnecessary clutter in users' inboxes. In this article, we will build a spam email detection model that classifies emails as Spam or Ham (Not Spam) using TensorFlow, one
    5 min read
    SMS Spam Detection using TensorFlow in Python
    In today's society, practically everyone has a mobile phone, and they all get communications (SMS/ email) on their phone regularly. But the essential point is that majority of the messages received will be spam, with only a few being ham or necessary communications. Scammers create fraudulent text m
    7 min read
    Python | Classify Handwritten Digits with Tensorflow
    Classifying handwritten digits is the basic problem of the machine learning and can be solved in many ways here we will implement them by using TensorFlowUsing a Linear Classifier Algorithm with tf.contrib.learn linear classifier achieves the classification of handwritten digits by making a choice b
    4 min read
    Recognizing HandWritten Digits in Scikit Learn
    Scikit learn is one of the most widely used machine learning libraries in the machine learning community the reason behind that is the ease of code and availability of approximately all functionalities which a machine learning developer will need to build a machine learning model. In this article, w
    10 min read
    Identifying handwritten digits using Logistic Regression in PyTorch
    Logistic Regression is a very commonly used statistical method that allows us to predict a binary output from a set of independent variables. The various properties of logistic regression and its Python implementation have been covered in this article previously. Now, we shall find out how to implem
    7 min read
    Customer Churn Analysis Prediction - Python
    Customer churn occurs when a customer stops using a company’s service lead to revenue loss. Analyzing churn helps businesses understand why customers leave and how to improve retention. High churn rates can affect revenue and business growth. By analyzing churn patterns businesses can take proactive
    4 min read
    Online Payment Fraud Detection using Machine Learning in Python
    As we are approaching modernity, the trend of paying online is increasing tremendously. It is very beneficial for the buyer to pay online as it saves time, and solves the problem of free money. Also, we do not need to carry cash with us. But we all know that Good thing are accompanied by bad things.
    5 min read
    Flipkart Reviews Sentiment Analysis using Python
    Sentiment analysis is a NLP task used to determine the sentiment behind textual data. In context of product reviews it helps in understanding whether the feedback given by customers is positive, negative or neutral. It helps businesses gain valuable insights about customer experiences, product quali
    3 min read
    Loan Approval Prediction using Machine Learning
    Loans are a major requirement of the modern world. By this alone, banks receive a major portion of the total profit. It is beneficial for students to manage their education and living expenses, and for individuals to purchase various luxuries, such as houses and cars. But when it comes to deciding w
    4 min read
    Loan Eligibility Prediction using Machine Learning Models in Python
    Have you ever thought about the apps that can predict whether you will get your loan approved or not? In this article, we are going to develop one such model that can predict whether a person will get his/her loan approved or not by using some of the background information of the applicant like the
    5 min read
    Stock Price Prediction using Machine Learning in Python
    Machine learning proves immensely helpful in many industries in automating tasks that earlier required human labor one such application of ML is predicting whether a particular trade will be profitable or not.In this article, we will learn how to predict a signal that indicates whether buying a part
    8 min read
    Bitcoin Price Prediction using Machine Learning in Python
    Machine learning proves immensely helpful in many industries in automating tasks that earlier required human labor one such application of ML is predicting whether a particular trade will be profitable or not.In this article, we will learn how to predict a signal that indicates whether buying a part
    7 min read
    Handwritten Digit Recognition using Neural Network
    Handwritten digit recognition is a classic problem in machine learning and computer vision. It involves recognizing handwritten digits (0-9) from images or scanned documents. This task is widely used as a benchmark for evaluating machine learning models especially neural networks due to its simplici
    5 min read
    Parkinson Disease Prediction using Machine Learning - Python
    Parkinson's disease is a progressive neurological disorder that affects movement. Stiffening, tremors and slowing down of movements may be signs of Parkinson's disease. While there is no certain diagnostic test, but we can use machine learning in predicting whether a person has Parkinson's disease b
    8 min read
    Spaceship Titanic Project using Machine Learning - Python
    If you are a machine learning enthusiast you must have done the Titanic project in which you would have predicted whether a person will survive or not. Spaceship Titanic Project using Machine Learning in PythonIn this article, we will try to solve one such problem which is a slightly modified versio
    9 min read
    Rainfall Prediction using Machine Learning - Python
    Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department's prediction fails sometimes. In this article, we will learn how to build a machine-learning model which can predict whether there will be rainfall today o
    6 min read
    Autism Prediction using Machine Learning
    Autism is a neurological disorder that affects a person's ability to interact with others, make eye contact with others, learn and have other behavioral issue. However there is no certain way to tell whether a person has Autism or not because there are no such diagnostics methods available to diagno
    8 min read
    Predicting Stock Price Direction using Support Vector Machines
    We are going to implement an End-to-End project using Support Vector Machines to live Trade For us. You Probably must have Heard of the term stock market which is known to have made the lives of thousands and to have destroyed the lives of millions. If you are not familiar with the stock market you
    5 min read
    Fake News Detection Model using TensorFlow in Python
    Fake news is a type of misinformation that can mislead readers, influence public opinion, and even damage reputations. Detecting fake news prevents its spread and protects individuals and organizations. Media outlets often use these models to help filter and verify content, ensuring that the news sh
    5 min read
    CIFAR-10 Image Classification in TensorFlow
    Prerequisites:Image ClassificationConvolution Neural Networks including basic pooling, convolution layers with normalization in neural networks, and dropout.Data Augmentation.Neural Networks.Numpy arrays.In this article, we are going to discuss how to classify images using TensorFlow. Image Classifi
    8 min read
    Black and white image colorization with OpenCV and Deep Learning
    In this article, we'll create a program to convert a black & white image i.e grayscale image to a colour image. We're going to use the Caffe colourization model for this program. And you should be familiar with basic OpenCV functions and uses like reading an image or how to load a pre-trained mo
    3 min read
    ML | Breast Cancer Wisconsin Diagnosis using Logistic Regression
    Breast Cancer Wisconsin Diagnosis dataset is commonly used in machine learning to classify breast tumors as malignant (cancerous) or benign (non-cancerous) based on features extracted from breast mass images. In this article we will apply Logistic Regression algorithm for binary classification to pr
    5 min read
    ML | Cancer cell classification using Scikit-learn
    Machine learning is used in solving real-world problems including medical diagnostics. One such application is classifying cancer cells based on their features and determining whether they are 'malignant' or 'benign'. In this article, we will use Scikit-learn to build a classifier for cancer cell de
    3 min read
    ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation
    Dataset : It is given by Kaggle from UCI Machine Learning Repository, in one of its challenges. It is a dataset of Breast Cancer patients with Malignant and Benign tumor. K-nearest neighbour algorithm is used to predict whether is patient is having cancer (Malignant tumour) or not (Benign tumour). I
    3 min read
    Human Scream Detection and Analysis for Controlling Crime Rate - Project Idea
    Project Title: Human Scream Detection and Analysis for Controlling Crime Rate using Machine Learning and Deep Learning Crime is the biggest social problem of our society which is spreading day by day. Thousands of crimes are committed every day, and still many are occurring right now also all over t
    6 min read
    Multiclass image classification using Transfer learning
    Image classification is one of the supervised machine learning problems which aims to categorize the images of a dataset into their respective categories or labels. Classification of images of various dog breeds is a classic image classification problem. So, we have to classify more than one class t
    8 min read
    Intrusion Detection System Using Machine Learning Algorithms
    Problem Statement: The task is to build a network intrusion detector, a predictive model capable of distinguishing between bad connections, called intrusions or attacks, and good normal connections. Introduction:Intrusion Detection System is a software application to detect network intrusion using v
    11 min read
    Heart Disease Prediction using ANN
    Deep Learning is a technology of which mimics a human brain in the sense that it consists of multiple neurons with multiple layers like a human brain. The network so formed consists of an input layer, an output layer, and one or more hidden layers. The network tries to learn from the data that is fe
    3 min read

    Regression Projects

    IPL Score Prediction using Deep Learning
    In today’s world of cricket every run and decision can turn the game around. Using Deep Learning to predict IPL scores during live matches is becoming a game changer. This article shows how advanced algorithms help us to forecast scores with impressive accuracy, giving fans and analysts valuable ins
    7 min read
    Dogecoin Price Prediction with Machine Learning
    Dogecoin is a cryptocurrency, like Ethereum or Bitcoin — despite the fact that it's totally different than both of these famous coins. Dogecoin was initially made to some extent as a joke for crypto devotees and took its name from a previously well-known meme.In this article, we will be implementing
    4 min read
    Zillow Home Value (Zestimate) Prediction in ML
    In this article, we will try to implement a house price index calculator which revolutionized the whole real estate industry in the US. This will be a regression task in which we have been provided with logarithm differences between the actual and the predicted prices of those homes by using a bench
    6 min read
    Calories Burnt Prediction using Machine Learning
    In this article, we will learn how to develop a machine learning model using Python which can predict the number of calories a person has burnt during a workout based on some biological measures.Importing Libraries and DatasetPython libraries make it easy for us to handle the data and perform typica
    5 min read
    Vehicle Count Prediction From Sensor Data
    Sensors at road junctions collect vehicle count data at different times which helps transport managers make informed decisions. In this article we will predict vehicle count based on this sensor data using machine learning techniques.Implementation of Vehicle Count PredictionDataset which we will be
    3 min read
    Analyzing Selling Price of used Cars using Python
    Analyzing the selling price of used cars is essential for making informed decisions in the automotive market. Using Python, we can efficiently process and visualize data to uncover key factors influencing car prices. This analysis not only aids buyers and sellers but also enables predictive modeling
    4 min read
    Box Office Revenue Prediction Using Linear Regression in ML
    The objective of this project is to develop a machine learning model using Linear Regression to accurately predict the box office revenue of movies based on various available features. The model will be trained on a dataset containing historical movie data and will aim to identify key factors that i
    9 min read
    House Price Prediction using Machine Learning in Python
    House price prediction is a problem in the real estate industry to make informed decisions. By using machine learning algorithms we can predict the price of a house based on various features such as location, size, number of bedrooms and other relevant factors. In this article we will explore how to
    6 min read
    Linear Regression using Boston Housing Dataset - ML
    Boston Housing Data: This dataset was taken from the StatLib library and is maintained by Carnegie Mellon University. This dataset concerns the housing prices in the housing city of Boston. The dataset provided has 506 instances with 13 features.The Description of the dataset is taken from the below
    3 min read
    Stock Price Prediction Project using TensorFlow
    Stock price prediction is a challenging task in the field of finance with applications ranging from personal investment strategies to algorithmic trading. In this article we will explore how to build a stock price prediction model using TensorFlow and Long Short-Term Memory (LSTM) networks a type of
    5 min read
    Medical Insurance Price Prediction using Machine Learning - Python
    You must have heard some advertisements regarding medical insurance that promises to help financially in case of any medical emergency. One who purchases this type of insurance has to pay premiums monthly and this premium amount varies vastly depending upon various factors. Medical Insurance Price P
    7 min read
    Inventory Demand Forecasting using Machine Learning - Python
    Vendors selling everyday items need to keep their stock updated so that customers don’t leave empty-handed. Maintaining the right stock levels helps avoid shortages that disappoint customers and prevents overstocking which can increase costs. In this article we’ll learn how to use Machine Learning (
    6 min read
    Ola Bike Ride Request Forecast using ML
    From telling rickshaw-wala where to go, to tell him where to come we have grown up. Yes, we are talking about online cab and bike facility providers like OLA and Uber. If you had used this app some times then you must have paid some day less and someday more for the same journey. But have you ever t
    8 min read
    Waiter's Tip Prediction using Machine Learning
    If you have recently visited a restaurant for a family dinner or lunch and you have tipped the waiter for his generous behavior then this project might excite you. As in this article, we will try to predict what amount of tip a person will give based on his/her visit to the restaurant using some fea
    7 min read
    Predict Fuel Efficiency Using Tensorflow in Python
    Predicting fuel efficiency is a important task in automotive design and environmental sustainability. In this article we will build a fuel efficiency prediction model using TensorFlow one of the most popular deep learning libraries. We will use the Auto MPG dataset which contains features like engin
    5 min read
    Microsoft Stock Price Prediction with Machine Learning
    In this article, we will implement Microsoft Stock Price Prediction with a Machine Learning technique. We will use TensorFlow, an Open-Source Python Machine Learning Framework developed by Google. TensorFlow makes it easy to implement Time Series forecasting data. Since Stock Price Prediction is one
    5 min read
    Share Price Forecasting Using Facebook Prophet
    Time series forecast can be used in a wide variety of applications such as Budget Forecasting, Stock Market Analysis, etc. But as useful it is also challenging to forecast the correct projections, Thus can't be easily automated because of the underlying assumptions and factors. The analysts who prod
    6 min read
    Implementation of Movie Recommender System - Python
    Recommender Systems provide personalized suggestions for items that are most relevant to each user by predicting preferences according to user's past choices. They are used in various areas like movies, music, news, search queries, etc. These recommendations are made in two ways: Collaborative filte
    4 min read
    How can Tensorflow be used with abalone dataset to build a sequential model?
    In this article, we will learn how to build a sequential model using TensorFlow in Python to predict the age of an abalone. We may wonder what is an abalone. Answer to this question is that it is a kind of snail. Generally, the age of an Abalone is determined by the physical examination of the abalo
    7 min read

    Computer Vision Projects

    OCR of Handwritten digits | OpenCV
    OCR which stands for Optical Character Recognition is a computer vision technique used to identify the different types of handwritten digits that are used in common mathematics. To perform OCR in OpenCV we will use the KNN algorithm which detects the nearest k neighbors of a particular data point an
    2 min read
    Cartooning an Image using OpenCV - Python
    Instead of sketching images by hand we can use OpenCV to convert a image into cartoon image. In this tutorial you'll learn how to turn any image into a cartoon. We will apply a series of steps like:Smoothing the image (like a painting)Detecting edges (like a sketch)Combining both to get a cartoon ef
    2 min read
    Count number of Object using Python-OpenCV
    In this article, we will use image processing to count the number of Objects using OpenCV in Python.Google Colab link: https://colab.research.google.com/drive/10lVjcFhdy5LVJxtSoz18WywM92FQAOSV?usp=sharingModule neededOpenCv: OpenCv is an open-source library that is useful for computer vision applica
    2 min read
    Count number of Faces using Python - OpenCV
    Prerequisites: Face detection using dlib and openCV In this article, we will use image processing to detect and count the number of faces. We are not supposed to get all the features of the face. Instead, the objective is to obtain the bounding box through some methods i.e. coordinates of the face i
    3 min read
    Text Detection and Extraction using OpenCV and OCR
    Optical Character Recognition (OCR) is a technology used to extract text from images which is used in applications like document digitization, license plate recognition and automated data entry. In this article, we explore how to detect and extract text from images using OpenCV for image processing
    2 min read
    FaceMask Detection using TensorFlow in Python
    In this article, we’ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented. We’ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, we’ll proceed to i
    9 min read
    Dog Breed Classification using Transfer Learning
    In this tutorial, we will demonstrate how to build a dog breed classifier using transfer learning. This method allows us to use a pre-trained deep learning model and fine-tune it to classify images of different dog breeds. Why to use Transfer Learning for Dog Breed ClassificationTransfer learning is
    9 min read
    Flower Recognition Using Convolutional Neural Network
    Convolutional Neural Network (CNN) are a type of deep learning model specifically designed for processing structured grid data such as images. In this article we will build a CNN model to classify different types of flowers from a dataset containing images of various flowers like roses, daisies, dan
    6 min read
    Emojify using Face Recognition with Machine Learning
    In this article, we will learn how to implement a modification app that will show an emoji of expression which resembles the expression on your face. This is a fun project based on computer vision in which we use an image classification model in reality to classify different expressions of a person.
    7 min read
    Cat & Dog Classification using Convolutional Neural Network in Python
    Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi
    5 min read
    Traffic Signs Recognition using CNN and Keras in Python
    We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us
    5 min read
    Lung Cancer Detection using Convolutional Neural Network (CNN)
    Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo
    7 min read
    Lung Cancer Detection Using Transfer Learning
    Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.In this article, we will learn how to build a classifier using the Transfer Learning technique
    8 min read
    Pneumonia Detection using Deep Learning
    In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta
    7 min read
    Detecting Covid-19 with Chest X-ray
    COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic
    9 min read
    Skin Cancer Detection using TensorFlow
    In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us
    5 min read
    Age Detection using Deep Learning in OpenCV
    The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'
    5 min read
    Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
    In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff
    4 min read
    Detecting COVID-19 From Chest X-Ray Images using CNN
    A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T
    5 min read
    Image Segmentation Using TensorFlow
    Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,
    7 min read
    License Plate Recognition with OpenCV and Tesseract OCR
    License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar
    5 min read
    Detect and Recognize Car License Plate from a video in real time
    Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
    11 min read
    Residual Networks (ResNet) - Deep Learning
    After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble
    9 min read

    Natural Language Processing Projects

    Twitter Sentiment Analysis using Python
    This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
    10 min read
    Facebook Sentiment Analysis using python
    This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
    6 min read
    Next Sentence Prediction using BERT
    Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
    4 min read
    Hate Speech Detection using Deep Learning
    There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
    5 min read
    Image Caption Generator using Deep Learning on Flickr8K dataset
    Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
    12 min read
    Movie recommendation based on emotion in Python
    Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
    4 min read
    Speech Recognition in Python using Google Speech API
    Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
    2 min read
    Voice Assistant using python
    Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
    3 min read
    Human Activity Recognition - Using Deep Learning Model
    Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
    6 min read
    Fine-tuning BERT model for Sentiment Analysis
    Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
    6 min read
    Sentiment Classification Using BERT
    BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
    12 min read
    Sentiment Analysis with an Recurrent Neural Networks (RNN)
    Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
    5 min read
    Building an Autocorrector Using NLP in Python
    Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
    4 min read
    Python | NLP analysis of Restaurant reviews
    Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach
    7 min read
    Restaurant Review Analysis Using NLP and SQLite
    Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
    9 min read
    Twitter Sentiment Analysis using Python
    This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
    10 min read

    Clustering Projects

    Customer Segmentation using Unsupervised Machine Learning in Python
    Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning
    5 min read
    Music Recommendation System Using Machine Learning
    When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona
    4 min read
    K means Clustering – Introduction
    K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ
    4 min read
    Image Segmentation using K Means Clustering
    Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.  Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im
    2 min read

    Recommender System Project

geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences