Most Viewed Models of the Month
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19 models found.
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January 24, 2022 - MODEL_posted_by
Prasad Chaskar,
1,418 views, 1 like
In astronomy, stellar classification is the classification of stars based on their spectral characteristics. The... moreIn astronomy, stellar classification is the classification of stars based on their spectral characteristics. The classification scheme of galaxies, quasars, and stars is one of the most fundamental in astronomy. The early cataloguing of stars and their distribution in the sky has led to the understanding that they make up our own galaxy and, following the distinction that Andromeda was a separate galaxy to our own, numerous galaxies began to be surveyed as more powerful telescopes were built. This dataset aims to classification stars, galaxies, and quasars based on their spectral characteristics.Dataset Link: https://www.kaggle.com/fedesoriano/stellar-classification-dataset-sdss17Model Accuracy: 97%Class 0: GalaxyClass 1: QSOClass 2: StarClassification Matrix: less
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Employee attrition happens when your workforce shrinks over time due to unavoidable variables like employee resignation for... moreEmployee attrition happens when your workforce shrinks over time due to unavoidable variables like employee resignation for personal or professional reasons.The project aims to predict whether an employee will leave the company or not.The dataset includes '24' features and '1412' instances.The "Accuracy_score" metric has been used to measure the model's performance.
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A 'Pothole' is a depression in a road surface, generally asphalt pavement, where shattered bits of the pavement have been... moreA 'Pothole' is a depression in a road surface, generally asphalt pavement, where shattered bits of the pavement have been displaced by driving. Water in the underlying soil structure and vehicles moving through the affected region are the most common causes.
The project aims to detect images that have potholes in them.
The dataset includes two labels containing 1000 images each, i.e., "Normal" and "Pothole." Thus, there are a total of 2000 images in the dataset, which includes plain roads and damaged roads.
The "Accuracy_score" metric has been used to measure that model's performance. less
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For a traveller, it is important to know the fare value of a trip, and as prices of flight tickets vary abruptly, it becomes... moreFor a traveller, it is important to know the fare value of a trip, and as prices of flight tickets vary abruptly, it becomes hectic for a user to check different websites and use different deals.
The dataset includes prices of flight tickets for various airlines between March and June of 2019 and between various cities.
The project aims to predict Flight ticket prices as accurately as possible.
'r2_score' has been used to check the model's performance.
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The hotel industry is one of the faster-growing businesses of the tourism sector, especially with the rise of giant OTA that... moreThe hotel industry is one of the faster-growing businesses of the tourism sector, especially with the rise of giant OTA that makes booking a hotel as easy as it has ever been.
The Project’s aim is to predict whether a customer will cancel his/her’s hotel room booking or not.
The data contains booking information for a 'city hotel' and a 'resort hotel.' In addition, it contains information such as when the booking was made, length of stay, the number of adults, children, and babies, and the number of available parking spaces, among other things.
The "Accuracy_score" metric has been used to measure that model's performance. less
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Even as engineers, 'cracking' on the top of the structure frame was also a big problem. That is because 'cracks' may have a... moreEven as engineers, 'cracking' on the top of the structure frame was also a big problem. That is because 'cracks' may have a major effect on structural safety, serviceability, and reliability.
The project aim is to detect cracks in concrete building structures using deep learning.
Construction companies can use the project to detect cracks and help determine the health of a concrete structure.
The datasets contain images of various concrete surfaces with and without cracks. Each class has 19,950 images, i.e. total of 39,900 images with 227 x 227 pixels with RGB channels.
The "Accuracy_score" metric has been used to measure that model's performance. less
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In this project, classification model of emergency vehicle vs non emergency vehicle has been build.After implementing custom... moreIn this project, classification model of emergency vehicle vs non emergency vehicle has been build.After implementing custom model architecture and hyperparameter tuning, obtained accuracy is 84.5%.
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The project is about predicting the price of second hand cars. Problem-Lack of knowlege about the current price of a used... moreThe project is about predicting the price of second hand cars. Problem-Lack of knowlege about the current price of a used car in market often make it difficult for a person to buy or sell a car,Solution- Many application and websites have made it easy for people to sell or buy cars, this model helps in finding the best market price of a car using machine learning algorithm.Who can use it- people who want to check prices of a second hand car based on some required feature details.Dataset contains features like model_year, kilometers_driven, mileage, seats and manymoreIn this regression problem the algorithm used is RandomForestRegressor as
It reduces overfitting in decision trees and helps to improve the accuracy.
It works well with both categorical and continuous values. less
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September 9, 2021 - MODEL_posted_by
Dev Agrawal,
700 views, 1 like
Introduction:
Prediction of the natural language of a text can be an important step in a Natural Language Processing (NLP)... moreIntroduction:
Prediction of the natural language of a text can be an important step in a Natural Language Processing (NLP) use case. For use cases like translation or sentiment analysis, it is better to know the language of the text used in it. For example, if you go to google translate, translation of the text is followed by detecting the language.
DataSet:
Dataset link: https://www.kaggle.com/zarajamshaid/language-identification-datasst
WiLI-2018, the Wikipedia language identification benchmark dataset, contains 235000 paragraphs of 235 languages. Each language in this dataset contains 1000 rows/paragraphs.
Model used:
For feature extraction used Bag of Words. Bag of Words (BOW) is used to extract features from text documents that are used for training machine learning algorithms by creating a vocabulary of all the unique words occurring in all the documents in the training set. The bag of words model is when we use all the words of any article/paragraph/text to get a feature vector. The Count vectorizer is used for the N-gram approach which tells how many words are taken together as a single entity in the training set for classification. Training is done on the following machine learning algorithms:
Random Forest Classifier:
Logistic regression:
Training is evaluated on multiple models from Uni-gram to 10-gram word/char models, and fitted on the following machine learning algorithms.
Results:
The accuracy score on the test dataset for all the models created are as follows:After analysis, the final model used is a uni-gram model for the final predictions on the random text entered by users. The accuracy achieved by this model is 95%.NOTE: Greater the length of input text better the accuracy. less
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September 9, 2021 - MODEL_posted_by
Tarun Reddy,
786 views, 3 likes
PROBLEM STATEMENT:Our main goal is to determine a person's gender by training a model on their eyes. This model may make... morePROBLEM STATEMENT:Our main goal is to determine a person's gender by training a model on their eyes. This model may make gender prediction very easier. Even if we don't have complete access to a person's face, we might predict their gender.DATASET DESCRIPTON:The data was collected to train a model to distinguish between images containing Female eyes and images of Male eyes.The folder femaleeyes contains 5202 images and the folder maleeyes contains 6323 images for training and testing the model.MODEL ACCURACY: 93%DATASET SOURCE: https://ruskino.ru/ less
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August 31, 2021 - MODEL_posted_by
Tarun Reddy,
766 views, 5 likes
Problem Statement:Forest fire detection should be quick and precise, as they can create massive damage and destruction.As... moreProblem Statement:Forest fire detection should be quick and precise, as they can create massive damage and destruction.As the technology is developing, we can use Machine Learning techniques to detect the forest fires as early as possible and can control it.
Dataset Description:
The data was gathered in order to train a model that can identify between pictures that contain fire (images of fire) and regular images (images which aren't fire), hence the task is simply a binary classification problem. The data is divided into two folders: one is for outdoor fire photographs, which comprises 755 images, some of which have smoke, and another for non-fire images, which has 244 nature images.Model Accuracy: 91% less
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July 21, 2021 - MODEL_posted_by
Vinayak Singh,
642 views, 3 likes
Problem: Increase in water, air, and land pollution by trashes and garbage. Increment in landfills, Eutrophication, Animals... moreProblem: Increase in water, air, and land pollution by trashes and garbage. Increment in landfills, Eutrophication, Animals consuming the toxic wastes, Leachate, Toxin increment is significant and disastrous results of improper treatment of waste by homo-sapiens.Input: The user will upload the garbage image for classification and the image will be classified into two categories whether the uploaded image is Organic Waste or Recyclable Garbage.Dataset Link: https://www.kaggle.com/techsash/waste-classification-dataThe dataset contains 25077 images, where 85% was used for training and 15% for the test set.Model: VGG19Model Accuracy: 86.35F1 - Score: 88.82 less
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Problem StatementThis model was built in order to explore an application of MediaPipe known as MediaPipe Pose... moreProblem StatementThis model was built in order to explore an application of MediaPipe known as MediaPipe Pose Detection. Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control. For example, it can form the basis for yoga, dance, and fitness applications. It can also enable the overlay of digital content and information on top of the physical world in augmented reality.How does this model work?The solution utilizes a two-step detector-tracker ML pipeline, proven to be effective in our MediaPipe Hands and MediaPipe Face Mesh solutions. Using a detector, the pipeline first locates the person/pose region-of-interest (ROI) within the frame. The tracker subsequently predicts the pose landmarks within the ROI using the ROI-cropped frame as input.Output DescriptionThis model can detect 3 yoga poses as of now. The Mountain Pose, Tree Pose and The Downward-Facing Dog Pose. How to use the model?1) Choose the Yoga Pose that you want to predict.2) Click predict.3) Voila! You can see your output now!. less
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May 26, 2021 - MODEL_posted_by
Aarzoo Goel,
1,057 views, 3 likes
Used Car price prediction
Introduction:
Used car Price Prediction is used in predicting the price of a used car with... moreUsed Car price prediction
Introduction:
Used car Price Prediction is used in predicting the price of a used car with different features included like age of the car, how many km covered, Mileage, Fuel type, Owner, and many more.
New car prices are fixed by the manufacturers with additional costs that include government additional amounts in the form of taxes. So, buying a new car means investing a large amount of money and sometimes customers don’t have that many funds. So, nowadays used car sale is increasing and there are many different apps for the same. Used Car price prediction uses other models from where we get to know the best price for our cars. It tells us this according to its market price.
This model helps the client predict his car’s market price if he wants to sell or purchase a used car.
Data:
The dataset I used here is downloaded from Kaggle. It includes 14 different features like name, model, brand, owner type, mileage, Kilometers driven, engine, power, transmission, etc.
You Can download the dataset from the link provided: https://www.kaggle.com/avikasliwal/used-cars-price-prediction.
Data Pre Processing
Data Cleaning, Data pre-processing, Exploratory data analysis have been applied to data, and we will be discussing that in-depth further.
There is one more assumption for this dataset that the Price value should be greater than the New_Price as the Price is the actual price of the car and the New_Price is the price calculated but in this dataset, we see that the New_Price is greater than Price. So, we will assume the Price as the New_Price of the car and drop New_Price values.
Data cleaning includes to check the null values, deleting duplicate rows, imputation of null values and even change in column name, replacing of blank space, removing string part from integer type like from mileage column removing ‘km/h’, ’kmpl’, ‘CC’ from engine to get the integer or float values which can be computed.
data['Mileage'] = data['Mileage'].str.replace(r'kmpl', '')
data['Engine'] = data['Engine'].str.replace(r' CC', '')
data['Power'] = data['Power'].str.replace(r'bhp', '')
Every data needs to be pre-processed first. Different pre-processing functions are written like:
getCountOfMissingValuesPerColumn(): Gives us the count of missing values per column.
display(preprocessing.getCountOfMissingValuesPerColumn(data, exclude_Zero_percent=True))
convertObjectColumnsToCategory(): Every Object datatype column is converted into category type.
preprocessing.convertObjectColumnsToCategory(cols)
convertFloatColumnsToInt64(): every float type column in integer.
dropMissingColumnsByThreshold(): We dropped the missing values by putting some threshold, here I used above 80% of missing values columns should be dropped.
printValueCountsOfCatagoricalColumns(): To print how many values are there in categorical column.
In the dataset, there is a feature New_Price with maximum null values which is dropped using drop by a threshold value.
EDA: While exploring data, we will look at the different combinations of features with the help of visuals. This helps us to understand our dataset better and give us some hints about the pattern in the data.
Many graphs are used to understand the data like count plot, boxplot, bar plot, face grid, etc.
Feature Engineering: Most of the feature engineering tasks are covered in data pre-processing. Here we updated the categorical values to numbers. For e.g.:
data['Fuel_Type'] = data['Fuel_Type'].str.replace(r'LPG', '3')
data['Fuel_Type'] = data['Fuel_Type'].str.replace(r'Electric', '4')
data['Transmission'] = data['Transmission'].str.replace(r'Manual', '1')
data['Transmission'] = data['Transmission'].str.replace(r'Automatic', '0')
data['Owner_Type'] = data['Owner_Type'].str.replace(r'First', '1')
data['Owner_Type'] = data['Owner_Type'].str.replace(r'Second', '2')
Modeling:
This makes it easy to implement different models and get accurate values for its accuracy score, confusion matrix, TP, TN, FP, and FN values.
The main features used to calculate the price of a used car are: Year, Km drove, Fuel Type, Transmission, Owner Type, Mileage.
We used different models here like Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, Grid Search CV, ANN. I have split the dataset into the train and test with 80 and 20 respectively.
Linear Regression: Linear regression was the first type of regression analysis, used extensively in practical applications. Some models linearly depend on their unknown parameters and are easier to fit than models which non-linearly depend on their parameters and because of this statistical property of the resulting variable is easier to determine. Regression can be used to identify the effect independent variable(s) have on a dependent variable. Solves problems like finding the age, market spending, income, etc.
Grid Search CV: This is a method for adjusting the features in... less
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Heart disease is a fatal human disease, rapidly increases globally in both developedand undeveloped countries and... moreHeart disease is a fatal human disease, rapidly increases globally in both developedand undeveloped countries and consequently causes death.In this use case, the machine learning model predicts if a person will be affected bycardiovascular disease or not.
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