Skip to content

Courses
Tutorials
Practice

Data Science
Data Science Projects
Data Analysis
Data Visualization
Machine Learning
ML Projects
Deep Learning
NLP
Computer Vision
Artificial Intelligence

Identifying handwritten digits using Logistic Regression in PyTorch

Recognizing HandWritten Digits in Scikit Learn

Last Updated : 19 Sep, 2024

Comments

Improve

Suggest changes

Like Article

Like

Report

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, we will learn how can we use sklearn to train an MLP model on the handwritten digits dataset. Some of the other benefits are:

It provides classification, regression, and clustering algorithms such as the SVM algorithm, random forests, gradient boosting, and k-means.
It is also designed to operate with Python's scientific and numerical libraries NumPy and SciPy. Scikit-learn is a NumFOCUS project that has financial support.

Importing Libraries and Dataset

Let us begin by importing the model's required libraries and loading the dataset digits.

Python

# importing the hand written digit dataset
from sklearn import datasets

# digit contain the dataset
digits = datasets.load_digits()

# dir function use to display the attributes of the dataset
dir(digits)

Output:

['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']

Function to print a set of image

digits.image is a three-dimensional array. The first dimension indexes images, and we can see that there are 1797 in total.
The following two dimensions relate to each image's pixels' x and y coordinates.
Each image is 8x8 = 64 pixels in size. In other terms, this array may be represented in 3D as a stack of 8x8 pixel images.

Python

# outputting the picture value as a series of numbers
print(digits.images[0])

Output:

[[ 0.  0.  5. 13.  9.  1.  0.  0.]
 [ 0.  0. 13. 15. 10. 15.  5.  0.]
 [ 0.  3. 15.  2.  0. 11.  8.  0.]
 [ 0.  4. 12.  0.  0.  8.  8.  0.]
 [ 0.  5.  8.  0.  0.  9.  8.  0.]
 [ 0.  4. 11.  0.  1. 12.  7.  0.]
 [ 0.  2. 14.  5. 10. 12.  0.  0.]
 [ 0.  0.  6. 13. 10.  0.  0.  0.]]

The original digits had much higher resolution, and the resolution was reduced when preparing the dataset for scikit-learn to allow training a machine learning system to recognize these digits easier and faster. Because at such a low resolution, even a human would struggle to recognize some of the digits The low quality of the input photos will also limit our neural network in these settings. Is the neural network capable of doing at least as well as an individual? It would already be an accomplishment!

Python

# importing the matplotlib libraries pyplot function
import matplotlib.pyplot as plt
# defining the function plot_multi

def plot_multi(i):
    nplots = 16
    fig = plt.figure(figsize=(15, 15))
    for j in range(nplots):
        plt.subplot(4, 4, j+1)
        plt.imshow(digits.images[i+j], cmap='binary')
        plt.title(digits.target[i+j])
        plt.axis('off')
    # printing the each digits in the dataset.
    plt.show()

    plot_multi(0)

Output:

Batch of Hand Written Digit Dataset — A batch of 16 Hand Written Digits

Training Neural network with the dataset

A neural network is a set of algorithms that attempts to recognize underlying relationships in a batch of data using a technique similar to how the human brain works. In this context, neural networks are systems of neurons that might be organic or artificial in nature.

an input layer consisting of 64 nodes, one for each pixel in the input pictures They simply send their input value to the neurons of the following layer.
This is a dense neural network, meaning each node in each layer is linked to all nodes in the preceding and following levels.

The input layer expects a one-dimensional array, whereas the image datasets are two-dimensional. As a result, flattening all images process takes place:

Python

# converting the 2 dimensional array to one dimensional array
y = digits.target
x = digits.images.reshape((len(digits.images), -1))

# gives the  shape of the data
x.shape

Output:

(1797, 64)

The 8x8 images' two dimensions have been merged into a single dimension by composing the rows of 8 pixels one after the other. The first image, which we discussed before, is now represented as a 1-D array having 8x8 = 64 slots.

Python

# printing the one-dimensional array's values
x[0]

Output:

array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,
       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,
       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,
        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,
       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])

Data split for training and testing.

When machine learning algorithms are used to make predictions based on data that was not used to train the model, the train-test split process is used to measure their performance.

It is a quick and simple technique that allows you to compare the performance of algorithms for machine learning for your predictive modeling challenge.

Python

# Very first 1000 photographs and
# labels will be used in training.
x_train = x[:1000]
y_train = y[:1000]

# The leftover dataset will be utilised to
# test the network's performance later on.
x_test = x[1000:]
y_test = y[1000:]

Usage of Multi-Layer Perceptron classifier

MLP stands for multi-layer perceptron. It consists of densely connected layers that translate any input dimension to the required dimension. A multi-layer perception is a neural network with multiple layers. To build a neural network, we connect neurons so that their outputs become the inputs of other neurons.

Python

# importing the MLP classifier from sklearn
from sklearn.neural_network import MLPClassifier

# calling the MLP classifier with specific parameters
mlp = MLPClassifier(hidden_layer_sizes=(15,),
                    activation='logistic',
                    alpha=1e-4, solver='sgd',
                    tol=1e-4, random_state=1,
                    learning_rate_init=.1,
                    verbose=True)

Now is the time to train our MLP model on the training data.

Python

mlp.fit(x_train, y_train)

Output:

Iteration 1, loss = 2.22958289
Iteration 2, loss = 1.91207743
Iteration 3, loss = 1.62507727
Iteration 4, loss = 1.32649842
Iteration 5, loss = 1.06100535
Iteration 6, loss = 0.83995513
Iteration 7, loss = 0.67806075
Iteration 8, loss = 0.55175832
Iteration 9, loss = 0.45840445
Iteration 10, loss = 0.39149735
Iteration 11, loss = 0.33676351
Iteration 12, loss = 0.29059880
Iteration 13, loss = 0.25437208
Iteration 14, loss = 0.22838372
Iteration 15, loss = 0.20200554
Iteration 16, loss = 0.18186565
Iteration 17, loss = 0.16461183
Iteration 18, loss = 0.14990228
Iteration 19, loss = 0.13892154
Iteration 20, loss = 0.12833784
Iteration 21, loss = 0.12138920
Iteration 22, loss = 0.11407971
Iteration 23, loss = 0.10677664
Iteration 24, loss = 0.10037149
Iteration 25, loss = 0.09593187
Iteration 26, loss = 0.09250135
Iteration 27, loss = 0.08676698
Iteration 28, loss = 0.08356043
Iteration 29, loss = 0.08209789
Iteration 30, loss = 0.07649168
Iteration 31, loss = 0.07410898
Iteration 32, loss = 0.07126869
Iteration 33, loss = 0.06926956
Iteration 34, loss = 0.06578496
Iteration 35, loss = 0.06374913
Iteration 36, loss = 0.06175492
Iteration 37, loss = 0.05975664
Iteration 38, loss = 0.05764485
Iteration 39, loss = 0.05623663
Iteration 40, loss = 0.05420966
Iteration 41, loss = 0.05413911
Iteration 42, loss = 0.05256140
Iteration 43, loss = 0.05020265
Iteration 44, loss = 0.04902779
Iteration 45, loss = 0.04788382
Iteration 46, loss = 0.04655532
Iteration 47, loss = 0.04586089
Iteration 48, loss = 0.04451758
Iteration 49, loss = 0.04341598
Iteration 50, loss = 0.04238096
Iteration 51, loss = 0.04162200
Iteration 52, loss = 0.04076839
Iteration 53, loss = 0.04003180
Iteration 54, loss = 0.03907774
Iteration 55, loss = 0.03815565
Iteration 56, loss = 0.03791975
Iteration 57, loss = 0.03706276
Iteration 58, loss = 0.03617874
Iteration 59, loss = 0.03593227
Iteration 60, loss = 0.03504175
Iteration 61, loss = 0.03441259
Iteration 62, loss = 0.03397449
Iteration 63, loss = 0.03326990
Iteration 64, loss = 0.03305025
Iteration 65, loss = 0.03244893
Iteration 66, loss = 0.03191504
Iteration 67, loss = 0.03132169
Iteration 68, loss = 0.03079707
Iteration 69, loss = 0.03044946
Iteration 70, loss = 0.03005546
Iteration 71, loss = 0.02960555
Iteration 72, loss = 0.02912799
Iteration 73, loss = 0.02859103
Iteration 74, loss = 0.02825959
Iteration 75, loss = 0.02788968
Iteration 76, loss = 0.02748725
Iteration 77, loss = 0.02721247
Iteration 78, loss = 0.02686225
Iteration 79, loss = 0.02635636
Iteration 80, loss = 0.02607439
Iteration 81, loss = 0.02577613
Iteration 82, loss = 0.02553642
Iteration 83, loss = 0.02518749
Iteration 84, loss = 0.02484300
Iteration 85, loss = 0.02455379
Iteration 86, loss = 0.02432480
Iteration 87, loss = 0.02398548
Iteration 88, loss = 0.02376004
Iteration 89, loss = 0.02341261
Iteration 90, loss = 0.02318255
Iteration 91, loss = 0.02296065
Iteration 92, loss = 0.02274048
Iteration 93, loss = 0.02241054
Iteration 94, loss = 0.02208181
Iteration 95, loss = 0.02190861
Iteration 96, loss = 0.02174404
Iteration 97, loss = 0.02156939
Iteration 98, loss = 0.02119768
Iteration 99, loss = 0.02101874
Iteration 100, loss = 0.02078230
Iteration 101, loss = 0.02061573
Iteration 102, loss = 0.02039802
Iteration 103, loss = 0.02017245
Iteration 104, loss = 0.01997162
Iteration 105, loss = 0.01989280
Iteration 106, loss = 0.01963828
Iteration 107, loss = 0.01941850
Iteration 108, loss = 0.01933154
Iteration 109, loss = 0.01911473
Iteration 110, loss = 0.01905371
Iteration 111, loss = 0.01876085
Iteration 112, loss = 0.01860656
Iteration 113, loss = 0.01848655
Iteration 114, loss = 0.01834844
Iteration 115, loss = 0.01818981
Iteration 116, loss = 0.01798523
Iteration 117, loss = 0.01783630
Iteration 118, loss = 0.01771441
Iteration 119, loss = 0.01749814
Iteration 120, loss = 0.01738339
Iteration 121, loss = 0.01726549
Iteration 122, loss = 0.01709638
Iteration 123, loss = 0.01698340
Iteration 124, loss = 0.01684606
Iteration 125, loss = 0.01667016
Iteration 126, loss = 0.01654172
Iteration 127, loss = 0.01641832
Iteration 128, loss = 0.01630111
Iteration 129, loss = 0.01623051
Iteration 130, loss = 0.01612736
Iteration 131, loss = 0.01590220
Iteration 132, loss = 0.01582485
Iteration 133, loss = 0.01571372
Iteration 134, loss = 0.01560349
Iteration 135, loss = 0.01557688
Iteration 136, loss = 0.01534420
Iteration 137, loss = 0.01527883
Iteration 138, loss = 0.01517545
Iteration 139, loss = 0.01503663
Iteration 140, loss = 0.01501192
Iteration 141, loss = 0.01482535
Iteration 142, loss = 0.01471388
Iteration 143, loss = 0.01463948
Iteration 144, loss = 0.01454059
Iteration 145, loss = 0.01441742
Iteration 146, loss = 0.01431741
Iteration 147, loss = 0.01428414
Iteration 148, loss = 0.01416364
Iteration 149, loss = 0.01406742
Iteration 150, loss = 0.01402651
Iteration 151, loss = 0.01389720
Iteration 152, loss = 0.01381412
Iteration 153, loss = 0.01371300
Iteration 154, loss = 0.01362465
Iteration 155, loss = 0.01357048
Iteration 156, loss = 0.01348760
Iteration 157, loss = 0.01339543
Iteration 158, loss = 0.01331941
Iteration 159, loss = 0.01320812
Iteration 160, loss = 0.01315415
Iteration 161, loss = 0.01308279
Iteration 162, loss = 0.01302708
Iteration 163, loss = 0.01290042
Iteration 164, loss = 0.01289267
Iteration 165, loss = 0.01277558
Iteration 166, loss = 0.01277238
Iteration 167, loss = 0.01261308
Iteration 168, loss = 0.01260611
Iteration 169, loss = 0.01248789
Iteration 170, loss = 0.01239662
Iteration 171, loss = 0.01231743
Iteration 172, loss = 0.01227346
Iteration 173, loss = 0.01223136
Iteration 174, loss = 0.01217211
Iteration 175, loss = 0.01208682
Iteration 176, loss = 0.01204707
Iteration 177, loss = 0.01200225
Iteration 178, loss = 0.01188677
Iteration 179, loss = 0.01184993
Iteration 180, loss = 0.01175130
Iteration 181, loss = 0.01171178
Iteration 182, loss = 0.01166052
Iteration 183, loss = 0.01163843
Iteration 184, loss = 0.01154892
Iteration 185, loss = 0.01147629
Iteration 186, loss = 0.01142365
Iteration 187, loss = 0.01136608
Iteration 188, loss = 0.01128053
Iteration 189, loss = 0.01128869
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.

MLPClassifier
MLPClassifier(activation='logistic', hidden_layer_sizes=(15,),
              learning_rate_init=0.1, random_state=1, solver='sgd',
              verbose=True)

Above shown is the loss for the last five epochs of the MLPClassifier and its respective configuration.

Python

fig, axes = plt.subplots(1, 1)
axes.plot(mlp.loss_curve_, 'o-')
axes.set_xlabel("number of iteration")
axes.set_ylabel("loss")
plt.show()

Output:

Model Evaluation

Now let's check the performance of the model using the recognition dataset or it just has memorized it. We will do this by using the leftover testing data so, that we can check whether the model has learned the actual pattern in the digit

Python

predictions = mlp.predict(x_test)
predictions[:50]

Output:

array([1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5,
       4, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 3, 0, 1, 2, 3, 4,
       5, 6, 7, 8, 5, 0])

But the true labels or we can say that the ground truth labels were as shown below.

Python

y_test[:50]

Output:

array([1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5,
       4, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,
       5, 6, 7, 8, 9, 0])

So, by using the predicted labels and the ground truth labels we can find the accuracy of our model.

Python

# importing the accuracy_score from the sklearn
from sklearn.metrics import accuracy_score

# calculating the accuracy with y_test and predictions
accuracy_score(y_test, predictions)

Output:

0.9146800501882058

Get the complete notebook and dataset link here:

Notebook link : click here.

Identifying handwritten digits using Logistic Regression in PyTorch

J

jjmnrjayanthi26

Improve

Article Tags :

Technical Scripter
Machine Learning
AI-ML-DS
Technical Scripter 2022
Python scikit-module

Practice Tags :

Machine Learning

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

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

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

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

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

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

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

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.learnÂ linear classifier achieves the classification of handwritten digits by making a choice b

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

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

Customer Churn Analysis Prediction - Python

Customer churn occurs when a customer stops using a companyâ€™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

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.

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

Loan Approval Prediction using Machine Learning

LOANS are the major requirement of the modern world. By this only, Banks get a major part of the total profit. It is beneficial for students to manage their education and living expenses, and for people to buy any kind of luxury like houses, cars, etc. But when it comes to deciding whether the appli

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

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

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

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

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

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.Â Spaceship Titanic Project using Machine Learning in PythonIn this article, we will try to solve one such problem which is a slightly modified versio

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

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

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

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

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

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

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

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

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

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

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

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

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

Regression Projects

IPL Score Prediction using Deep Learning

In todayâ€™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

Dogecoin Price Prediction with Machine Learning

Dogecoin is a cryptocurrency, like Ethereum or Bitcoin â€” 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

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

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

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

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

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

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

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

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

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.Â Medical Insurance Price P

Inventory Demand Forecasting using Machine Learning - Python

Vendors selling everyday items need to keep their stock updated so that customers donâ€™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 weâ€™ll learn how to use Machine Learning (

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

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

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

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

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

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

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

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

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

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

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

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

FaceMask Detection using TensorFlow in Python

In this article, weâ€™ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented. Weâ€™ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, weâ€™ll proceed to i

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

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

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.

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

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

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

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

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

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

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

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'

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

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

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,

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

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

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

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. Itâ€™s also known as opinion mini

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

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

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

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

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

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, youâ€™ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require

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 weâ€™ll create a basic voice assistant using Python.Step 1: Install Required

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

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

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

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

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

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

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

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. Itâ€™s also known as opinion mini

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

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

K means Clustering â€“ 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

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.Â Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im

Recommender System Project

AI Driven Snake Game using Deep Q Learning

Content has been removed from this Article

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

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

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.

'); // $('.spinner-loading-overlay').show(); let script = document.createElement('script'); script.src = 'https://assets.geeksforgeeks.org/v2/editor-prod/static/js/bundle.min.js'; script.defer = true document.head.appendChild(script); script.onload = function() { suggestionModalEditor() //to add editor in suggestion modal if(loginData && loginData.premiumConsent){ personalNoteEditor() //to load editor in personal note } } script.onerror = function() { if($('.editorError').length){ $('.editorError').remove(); } var messageDiv = $('

').text('Editor not loaded due to some issues'); $('#suggestion-section-textarea').append(messageDiv); $('.suggest-bottom-btn').hide(); $('.suggestion-section').hide(); editorLoaded = false; } }); //suggestion modal editor function suggestionModalEditor(){ // editor params const params = { data: undefined, plugins: ["BOLD", "ITALIC", "UNDERLINE", "PREBLOCK"], } // loading editor try { suggestEditorInstance = new GFGEditorWrapper("suggestion-section-textarea", params, { appNode: true }) suggestEditorInstance._createEditor("") $('.spinner-loading-overlay:eq(0)').remove(); editorLoaded = true; } catch (error) { $('.spinner-loading-overlay:eq(0)').remove(); editorLoaded = false; } } //personal note editor function personalNoteEditor(){ // editor params const params = { data: undefined, plugins: ["UNDO", "REDO", "BOLD", "ITALIC", "NUMBERED_LIST", "BULLET_LIST", "TEXTALIGNMENTDROPDOWN"], placeholderText: "Description to be......", } // loading editor try { let notesEditorInstance = new GFGEditorWrapper("pn-editor", params, { appNode: true }) notesEditorInstance._createEditor(loginData&&loginData.user_personal_note?loginData.user_personal_note:"") $('.spinner-loading-overlay:eq(0)').remove(); editorLoaded = true; } catch (error) { $('.spinner-loading-overlay:eq(0)').remove(); editorLoaded = false; } } var lockedCasesHtml = `You can suggest the changes for now and it will be under 'My Suggestions' Tab on Write.

You will be notified via email once the article is available for improvement. Thank you for your valuable feedback!`; var badgesRequiredHtml = `It seems that you do not meet the eligibility criteria to create improvements for this article, as only users who have earned specific badges are permitted to do so.

However, you can still create improvements through the Pick for Improvement section.`; jQuery('.improve-header-sec-child').on('click', function(){ jQuery('.improve-modal--overlay').hide(); $('.improve-modal--suggestion').hide(); jQuery('#suggestion-modal-alert').hide(); }); $('.suggest-change_wrapper, .locked-status--impove-modal .improve-bottom-btn').on('click',function(){ // when suggest changes option is clicked $('.ContentEditable__root').text(""); $('.suggest-bottom-btn').html("Suggest changes"); $('.thank-you-message').css("display","none"); $('.improve-modal--improvement').hide(); $('.improve-modal--suggestion').show(); $('#suggestion-section-textarea').show(); jQuery('#suggestion-modal-alert').hide(); if(suggestEditorInstance !== null){ suggestEditorInstance.setEditorValue(""); } $('.suggestion-section').css('display', 'block'); jQuery('.suggest-bottom-btn').css("display","block"); }); $('.create-improvement_wrapper').on('click',function(){ // when create improvement option clicked then improvement reason will be shown if(loginData && loginData.isLoggedIn) { $('body').append('

'); $('.spinner-loading-overlay').show(); jQuery.ajax({ url: writeApiUrl + 'create-improvement-post/?v=1', type: "POST", contentType: 'application/json; charset=utf-8', dataType: 'json', xhrFields: { withCredentials: true }, data: JSON.stringify({ gfg_id: post_id }), success:function(result) { $('.spinner-loading-overlay:eq(0)').remove(); $('.improve-modal--overlay').hide(); $('.unlocked-status--improve-modal-content').css("display","none"); $('.create-improvement-redirection-to-write').attr('href',writeUrl + 'improve-post/' + `${result.id}` + '/', '_blank'); $('.create-improvement-redirection-to-write')[0].click(); }, error:function(e) { showErrorMessage(e.responseJSON,e.status) }, }); } else { if(loginData && !loginData.isLoggedIn) { $('.improve-modal--overlay').hide(); if ($('.header-main__wrapper').find('.header-main__signup.login-modal-btn').length) { $('.header-main__wrapper').find('.header-main__signup.login-modal-btn').click(); } return; } } }); $('.left-arrow-icon_wrapper').on('click',function(){ if($('.improve-modal--suggestion').is(":visible")) $('.improve-modal--suggestion').hide(); else{ } $('.improve-modal--improvement').show(); }); const showErrorMessage = (result,statusCode) => { if(!result) return; $('.spinner-loading-overlay:eq(0)').remove(); if(statusCode == 403) { $('.improve-modal--improve-content.error-message').html(result.message); jQuery('.improve-modal--overlay').show(); jQuery('.improve-modal--improvement').show(); $('.locked-status--impove-modal').css("display","block"); $('.unlocked-status--improve-modal-content').css("display","none"); $('.improve-modal--improvement').attr("status","locked"); return; } } function suggestionCall() { var editorValue = suggestEditorInstance.getValue(); var suggest_val = $(".ContentEditable__root").find("[data-lexical-text='true']").map(function() { return $(this).text().trim(); }).get().join(' '); suggest_val = suggest_val.replace(/\s+/g, ' ').trim(); var array_String= suggest_val.split(" ") //array of words var gCaptchaToken = $("#g-recaptcha-response-suggestion-form").val(); var error_msg = false; if(suggest_val != "" && array_String.length >=4){ if(editorValue.length { jQuery('.ContentEditable__root').focus(); jQuery('#suggestion-modal-alert').hide(); }, 3000); } } document.querySelector('.suggest-bottom-btn').addEventListener('click', function(){ jQuery('body').append('

'); jQuery('.spinner-loading-overlay').show(); if(loginData && loginData.isLoggedIn) { suggestionCall(); return; } // script for grecaptcha loaded in loginmodal.html and call function to set the token setGoogleRecaptcha(); }); $('.improvement-bottom-btn.create-improvement-btn').click(function() { //create improvement button is clicked $('body').append('

'); $('.spinner-loading-overlay').show(); // send this option via create-improvement-post api jQuery.ajax({ url: writeApiUrl + 'create-improvement-post/?v=1', type: "POST", contentType: 'application/json; charset=utf-8', dataType: 'json', xhrFields: { withCredentials: true }, data: JSON.stringify({ gfg_id: post_id }), success:function(result) { $('.spinner-loading-overlay:eq(0)').remove(); $('.improve-modal--overlay').hide(); $('.create-improvement-redirection-to-write').attr('href',writeUrl + 'improve-post/' + `${result.id}` + '/', '_blank'); $('.create-improvement-redirection-to-write')[0].click(); }, error:function(e) { showErrorMessage(e.responseJSON,e.status); }, }); });

"For an ad-free experience and exclusive features, subscribe to our Premium Plan!"

Continue without supporting

`; $('body').append(adBlockerModal); $('body').addClass('body-for-ad-blocker'); const modal = document.getElementById("adBlockerModal"); modal.style.display = "block"; } function handleAdBlockerClick(type){ if(type == 'disabled'){ window.location.reload(); } else if(type == 'info'){ document.getElementById("ad-blocker-div").style.display = "none"; document.getElementById("ad-blocker-info-div").style.display = "flex"; handleAdBlockerIconClick(0); } } var lastSelected= null; //Mapping of name and video URL with the index. const adBlockerVideoMap = [ ['Ad Block Plus','https://media.geeksforgeeks.org/auth-dashboard-uploads/abp-blocker-min.mp4'], ['Ad Block','https://media.geeksforgeeks.org/auth-dashboard-uploads/Ad-block-min.mp4'], ['uBlock Origin','https://media.geeksforgeeks.org/auth-dashboard-uploads/ub-blocke-min.mp4'], ['uBlock','https://media.geeksforgeeks.org/auth-dashboard-uploads/U-blocker-min.mp4'], ] function handleAdBlockerIconClick(currSelected){ const videocontainer = document.getElementById('ad-blocker-info-div-gif'); const videosource = document.getElementById('ad-blocker-info-div-gif-src'); if(lastSelected != null){ document.getElementById("ad-blocker-info-div-icons-"+lastSelected).style.backgroundColor = "white"; document.getElementById("ad-blocker-info-div-icons-"+lastSelected).style.borderColor = "#D6D6D6"; } document.getElementById("ad-blocker-info-div-icons-"+currSelected).style.backgroundColor = "#D9D9D9"; document.getElementById("ad-blocker-info-div-icons-"+currSelected).style.borderColor = "#848484"; document.getElementById('ad-blocker-info-div-name-span').innerHTML = adBlockerVideoMap[currSelected][0] videocontainer.pause(); videosource.setAttribute('src', adBlockerVideoMap[currSelected][1]); videocontainer.load(); videocontainer.play(); lastSelected = currSelected; }

What kind of Experience do you want to share?

Interview Experiences

Admission Experiences

Career Journeys

Work Experiences

Campus Experiences

Competitive Exam Experiences