![\"machine](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/11\/machine-learning-datasets-for-beginners-1.jpg\")
<\/a><\/p>\n1. Mall Customers Dataset<\/h4>\n
The Mall customers dataset contains information about people visiting the mall. The dataset has gender, customer id, age, annual income, and spending score. It collects insights from the data and group customers based on their behaviors.<\/p>\n
1.1 Data Link:<\/strong>\u00a0mall customers dataset<\/a><\/p>\n 1.2 Data Science Project Idea:<\/strong>\u00a0Segment the customers based on the age, gender, interest. Customer segmentation is an important practise of dividing customers base into individual groups that are similar. It is useful in customised marketing.<\/p>\n 1.3 Source Code:<\/strong>\u00a0Customer Segmentation Project with Machine Learning<\/a><\/p>\n The iris dataset is a simple and beginner-friendly dataset that contains information about the flower petal and sepal sizes. The dataset has 3 classes with 50 instances in each class, therefore, it contains 150 rows with only 4 columns.<\/p>\n 2.1 Data Link:<\/strong>\u00a0Iris dataset<\/a><\/p>\n 2.2 Data Science Project Idea:<\/strong>\u00a0Implement a machine learning classification or regression model on the dataset. Classification is the task of separating items into its corresponding class.<\/p>\n This is a database of handwritten digits. It contains 60,000 training images and 10,000 testing images. This is a perfect dataset to start implementing image classification where you can classify a digit from 0 to 9.<\/p>\n 3.1 Data Link:<\/strong> MNIST dataset<\/a><\/p>\n 3.2 Data Science Project Idea:<\/strong> Implement a machine learning classification algorithm on image to recognize handwritten digits from a paper.<\/p>\n 3.3 Source Code:\u00a0<\/strong>Handwritten Digit Recognition with Deep Learning<\/a><\/p>\n This is a popular dataset used in pattern recognition. It contains information about the different houses in Boston based on crime rate, tax, number of rooms, etc. It has 506 rows and 14 different variables in columns. You can use this dataset to predict house prices.<\/p>\n 4.1 Data Link:<\/strong>\u00a0Boston dataset<\/a><\/p>\n 4.2 Data Science Project Idea:<\/strong>\u00a0Predict the housing prices of a new house using linear regression. Linear regression is used to predict values of unknown input when the data has some linear relationship between input and output variables.<\/p>\n It is a CSV file that has 7796 rows with 4 columns. The first column identifies news, second for the title, third for news text and fourth is the label TRUE or FAKE.<\/p>\n 5.1 Data Link:<\/strong>\u00a0Fake news detection dataset<\/a><\/p>\n 5.2 Data Science Project Idea:<\/strong>\u00a0Build a fake news detection model with Passive Aggressive Classifier algorithm. The Passive Aggressive algorithm can classify massive streams of data, it can be implemented quickly.<\/p>\n 5.3 Source Code: <\/strong>Fake News Detection Python Project<\/a><\/p>\n The dataset contains different chemical information about wine. It has 4898 instances with 14 variables each. The dataset is good for classification and regression tasks. The model can be used to predict wine quality.<\/p>\n 6.1 Data Link:<\/strong>\u00a0Wine quality dataset<\/a><\/p>\n 6.2 Data Science Project Idea:<\/strong> Perform various different machine learning algorithms<\/strong><\/em><\/a> like regression, decision tree, random forests, etc and\u00a0differentiate between the models and analyse their performances.<\/p>\n This is a simple dataset to start with. It contains only the height (inches) and weights (pounds) of 25,000 different humans of 18 years of age. This dataset can be used to build a model that can predict the heights or weights of a human.<\/p>\n 7.1 Data Link:<\/strong>\u00a0Heights & weights dataset<\/a><\/p>\n 7.2 Data Science Project Idea:<\/strong>\u00a0Build a predictive model for determining height or weight of a person. Implement a linear regression model that will be used for predicting height or weight.<\/p>\n Parkinson is a nervous system disorder that affects movement. The dataset contains 195 records of people with 23 different attributes which contain biomedical measurements. The data is used to separate healthy people from people with Parkinson\u2019s disease.<\/p>\n 8.1 Data Link:<\/strong>\u00a0Parkinson dataset<\/a><\/p>\n 8.2 Data Science Project Idea:<\/strong>\u00a0The model can be used to differentiate healthy people from people having Parkinson’s disease. The algorithm that is useful for this purpose is XGboost which stands for extreme gradient boosting, it is based on decision trees.<\/p>\n 8.3 Source Code:<\/strong> Machine Learning Project on Detecting Parkinson’s Disease<\/a><\/p>\n On 15 April 1912, the unsinkable Titanic ship sank and killed 1502 passengers out of 2224. The dataset contains information like name, age, sex, number of siblings aboard, etc of about 891 passengers in the training set and 418 passengers in the testing set.<\/p>\n 9.1 Data Link:\u00a0<\/strong>Titanic dataset<\/a><\/p>\n 9.2 Data Science Project Idea:<\/strong>\u00a0Build a fun model to predict whether a person would have survived on the Titanic or not. You can use linear regression for this purpose.<\/p>\n The dataset has information of about 4.5 million uber pickups in New York City from April 2014 to September 2014 and 14million more from January 2015 to June 2015. Users can perform data analysis and gather insights from the data.<\/p>\n 10.1 Data Link:<\/strong>\u00a0Uber pickups dataset<\/a><\/p>\n 10.2 Data Science Project Idea:\u00a0<\/strong>To analyze the data of the customer rides and visualize the data to find insights that can help improve business. Data analysis and visualization is an important part of data science. They are used to gather insights from the data and with visualization you can get quick information from the data.<\/p>\n 10.3 Source Code: <\/strong>Uber Data Analysis Project in R<\/a><\/p>\n The dataset contains images of character symbols used in the English and Kannada languages. It has 64 classes (0-9, A-Z, a-z), 7.7k characters from natural images, 3.4k hand-drawn characters, and 62k computer-synthesized fonts.<\/p>\n 11.1 Data Link:<\/strong>\u00a0Chars 74k dataset<\/a><\/p>\n 11.2 Data Science Project Idea:<\/strong>\u00a0Implement character recognition in natural languages. Character recognition is the process of automatically identifying characters from written papers or printed texts.<\/p>\n The dataset contains transactions made by credit cards, they are labeled as fraudulent or genuine. This is important for companies that have transaction systems to build a model for detecting fraudulent activities.<\/p>\n 12.1 Data Link:<\/strong>\u00a0Credit card fraud detection dataset<\/a><\/p>\n 12.2 Data Science Project Idea:<\/strong>\u00a0Implement different algorithms like decision trees, logistic regression, and artificial neural networks to see which gives better accuracy. Compare the results of each algorithm and understand the behavior of models.<\/p>\n 12.3 Source Code:<\/strong>\u00a0Credit Card Fraud Detection Machine Learning Project<\/a><\/p>\n 13 Chatbot Intents Dataset<\/strong><\/p>\n The dataset is a JSON file that contains different tags like greetings, goodbye, hospital_search, pharmacy_search, etc. Each tag contains a list of patterns a user can ask and the responses a chatbot can respond according to that pattern. The dataset is good for understanding how chatbot data works.<\/p>\n 13.1 Data Link:<\/strong> Intents JSON Dataset<\/a><\/p>\n 13.2 Data Science Project Idea:\u00a0<\/strong>Tweak and expand the data with your observations to build and understand the working of a chatbot in organizations. A chatbot requires you to understand Natural language processing concepts.<\/p>\n 13.3 Source Code: <\/strong>Chatbot Project in\u00a0Python<\/a><\/p>\n This consists of Diverse synthetic and real-life datasets for machine learning.<\/p>\n 14.1 Data Link: <\/strong>AI generated Faces Dataset<\/a><\/p>\n This Enron dataset is popular in natural language processing. It contains around 0.5 million emails of over 150 users out of which most of the users are the senior management of Enron. The size of the data is around 432Mb.<\/p>\n 1.1 Data Link:<\/strong>\u00a0Enron email dataset<\/a><\/p>\n 1.2 Machine Learning Project Idea:<\/strong>\u00a0Use k-means clustering to build a model to detect fraudulent activities. K-means clustering is a popular unsupervised learning algorithm. It partitions the observations into k number of clusters by observing similar patterns in the data.<\/p>\n The yelp made their dataset publicly available but you have to fill a form first to access the data. It contains 1.2 million tips by 1.6 million users, over 1.2 million business attributes and photos for natural language processing tasks.<\/p>\n 2.1 Data Link:<\/strong>\u00a0Yelp dataset<\/a><\/p>\n 2.2 Machine Learning Project Idea:<\/strong>\u00a0You can build a model which can detect whether a restaurant\u2019s review is fake or real. With text processing and additional features in dataset you can build a SVM model that can classify reviews as fake or real.<\/p>\n Jeopardy! is an American television game show in which general knowledge questions are asked with a twist. The dataset contains 200k+ questions and answers in a CSV or JSON file.<\/p>\n 3.1 Data Link:<\/strong> Jeopardy dataset<\/a><\/p>\n 3.2 Machine Learning Project Idea:<\/strong>\u00a0We Build a question answering system and implement in a bot that can play the game of jeopardy with users. The bot can be used on any platform like Telegram, discord, reddit, etc.<\/p>\n This is a portal to a collection of rich datasets that were used in lab research projects at UCSD. It contains various datasets from popular websites like Goodreads book reviews, Amazon product reviews, bartending data, data from social media, etc that are used in building a recommender system.<\/p>\n 4.1 Data Link:<\/strong>\u00a0Recommender systems dataset<\/a><\/p>\n 4.2 Machine Learning Project Idea:<\/strong>\u00a0Build a product recommendation system like Amazon. A recommendation system can suggest you products, movies, etc based on your interests and the things you like and have used earlier.<\/p>\n 4.3 Source Code:<\/strong> Movie Recommendation System Project in R<\/a><\/p>\n Classifying emails as spam or non-spam is a very common and useful task. The dataset contains 4601 emails and 57 meta-information about the emails. You can build models to filter out the spam.<\/p>\n 5.1 Data Link:<\/strong> UCI spambase dataset<\/a><\/p>\n 5.2 Machine Learning Project Idea:<\/strong> You can build a model that can identify your emails as spam or non-spam.<\/p>\n The Flickr 30k dataset is similar to the Flickr 8k dataset and it contains more labeled images. This has over 30,000 images and their captions. This dataset is used to build more accurate models than the Flickr 8k dataset.<\/p>\n 6.1 Data Link:<\/strong>\u00a0Flickr image dataset<\/a><\/p>\n 6.2 Machine Learning Project Idea:<\/strong>\u00a0Use the same model from Flickr 8k and make it more accurate with more training data. The CNN model is great for extracting features from the image and then we feed the features to a recurrent neural network that will generate caption.<\/p>\n The large movie review dataset consists of movie reviews from IMDB website with over 25,000 reviews for training and 25,000 for the testing set.<\/p>\n 7.1 Data Link:<\/strong>\u00a0IMDB reviews dataset<\/a><\/p>\n 7.2 Machine Learning Project Idea:<\/strong>\u00a0Perform Sentiment analysis on the data to see the statistics of what type of movie do users like. Sentiment analysis is the process of analysing the textual data and identifying the emotion of the user, Positive or Negative.<\/p>\n 7.3 Source Code:<\/strong> Sentiment Analysis Data Science Project<\/a><\/p>\n Microsoft’s COCO is a huge database for object detection, segmentation and image captioning tasks. It has around 1.5 million labeled images. The dataset is great for building production-ready models.<\/p>\n 8.1 Data Link:<\/strong>\u00a0MS COCO dataset<\/a><\/p>\n 8.2 Machine Learning Project Idea:<\/strong>\u00a0Detect objects from the image and then generate captions for them. LSTM (Long short term memory) network is responsible for generating sentences in English and CNN is used to extract features from image. To build a caption generator we have to combine these two models.<\/p>\n The Flickr 8k dataset contains 8000 images and each image is labeled with 5 different captions. The dataset is used to build an image caption generator.<\/p>\n 9.1 Data Link:<\/strong>\u00a0Flickr 8k dataset<\/a><\/p>\n 9.2 Machine Learning Project Idea:<\/strong>\u00a0Build an image caption generator using CNN-RNN model. An image caption generator model is able to analyse features of the image and generate english like sentence that describes the image.<\/p>\n 9.3 Source Code:<\/strong> Image Caption Generator Python Project<\/a><\/p>\n These are two datasets, the CIFAR-10 dataset contains 60,000 tiny images of 32*32 pixels. They are labeled from 0-9 and each digit is representing a class. The CIFAR-100 is similar to the CIFAR-10 dataset but the difference is that it has 100 classes instead of 10. This dataset is good for implementing image classification.<\/p>\n 1.1 Data Link:<\/strong>\u00a0CIFAR dataset<\/a><\/p>\n 1.2 Artificial Intelligence Project Idea:<\/strong>\u00a0Perform image classification on different objects and build a model. In image classification, we take image as an input and the goal is to classify in which category the image belongs to.<\/p>\n The GTSRB dataset contains around 50,000 images of traffic signs belonging to 43 different classes and contains information on the bounding box of each sign. The dataset is used for multiclass classification.<\/p>\n 2.1 Data Link:<\/strong> GTSRB dataset<\/a><\/p>\n 2.2 Artificial Intelligence Project Idea:<\/strong>\u00a0Build a model using a deep learning framework that classifies traffic signs and also recognises the bounding box of signs. The traffic sign classification is also useful in autonomous vehicles for identifying signs and then take appropriate actions.<\/p>\n 2.3 Source Code:<\/strong>\u00a0Traffic Signs Recognition Python Project<\/a><\/p>\n ImageNet is a large image database that is organized according to the wordnet hierarchy. It has over 100,000 phrases and an average of 1000 images per phrase. The size exceeds 150 GB. It is suitable for image recognition, face recognition, object detection, etc. It also hosts a challenging competition named ILSVRC for people to build more and more accurate models.<\/p>\n 3.1 Data Link:<\/strong>\u00a0Imagenet Dataset<\/a><\/p>\n 3.2 Artificial Intelligence Project Idea:<\/strong>\u00a0To implement image classification on this huge database and recognise objects. CNN model (Convolutional neural networks) are necessary for this project to get accurate results.<\/p>\n This dataset contains 2,77,524 images of size 50\u00d750 extracted from 162 mount slide images of breast cancer specimens scanned at 40x. There are 1,98,738 negative tests and 78,786 positive tests with IDC.<\/p>\n 4.1 Data Link:<\/strong>\u00a0Breast histopathology dataset<\/a><\/p>\n 4.2 Artificial Intelligence Project Idea:<\/strong>\u00a0To build a model that can classify breast cancer. You build an image classification model with Convolutional neural networks.<\/p>\n 4.3 Source Code:<\/strong>\u00a0Breast Cancer Classification Python Project<\/a><\/p>\n This is an open-source dataset for Computer Vision projects. It contains high-quality pixel-level annotations of video sequences taken in 50 different city streets. The dataset is useful in semantic segmentation and training deep neural networks to understand the urban scene.<\/p>\n 5.1 Data Link:<\/strong>\u00a0Cityscapes dataset<\/a><\/p>\n 5.2 Artificial Intelligence Project Idea:<\/strong>\u00a0To perform image segmentation and detect different objects from a video on the road. Image segmentation is the process of digitally partitioning an image into various different categories like cars, buses, people, trees, roads, etc.<\/p>\n There are three different datasets for Kinetics: Kinetics 400, Kinetics 600 and Kinetics 700 dataset. This is a large scale dataset that contains a URL link to around 6.5Million high-quality videos.<\/p>\n 6.1 Data Link:<\/strong>\u00a0Kinetics dataset<\/a><\/p>\n 6.2 Artificial Intelligence Project Idea:<\/strong>\u00a0Build a human action recognition model and detect the action of a human. Human action recognition is recognized by a series of observations.<\/p>\n The MPII human pose dataset contains 25,000 images with over 40,000 people with annotated body joints. The overall dataset covers over 410 human activities. The dataset is 12.9 GB in size.<\/p>\n 7.1 Data Link: <\/strong>MPII human pose dataset<\/a><\/p>\n 7.2 Artificial Intelligence\u00a0Project Idea:<\/strong>\u00a0To detect different human poses based on the alignment of a person’s body joints. Human pose detection tracks every movement of the body. It is also known as the localization of human joints.<\/p>\n This is a huge high-quality video clips dataset that shows human performing actions like picking something, putting something down, opening something, closing something, etc.<\/p>\n It has 220,847 total number of videos.<\/p>\n 8.1 Data Link: <\/strong>Something-something dataset<\/a><\/p>\n 8.2 Artificial Intelligence\u00a0Project Idea:<\/strong>\u00a0To implement a human action recognition model and detect different activities performed by a human. The activities can be used in detecting activities while driving, surveillance activities, etc.<\/p>\n The object 365 dataset is a large collection of high-quality images with bounding boxes of objects. It has 365 objects, 600k images, and 10 million bounding boxes. This is good for making object detection models.<\/p>\n 9.1 Data Link:<\/strong>\u00a0Object 365 dataset<\/a><\/p>\n 9.2 Artificial Intelligence Project Idea:<\/strong>\u00a0Classify images captured from the camera and detect objects present in the image. Object detection deals with recognizing which object is present in the image along with the coordinates of the object.<\/p>\n The dataset contains images paired with their contour drawings. It has 1000 outdoor drawings, each image has 5 rough contour drawings that represent the outline of the image.<\/p>\n 10.1 Data Link:<\/strong>\u00a0Photo sketching dataset<\/a><\/p>\n 10.2 Artificial Intelligence Project Idea:<\/strong>\u00a0Build a model that can develop sketches automatically from the images. This will take an image as an input and generate a sketch image using computer vision techniques.<\/p>\n This dataset is publicly available that has 491 head CT scans with 193,317 slices. It contains opinions of three different radiologists on each image. The dataset can be used to build models that can detect bleeding, fractures and mass effect on the head.<\/p>\n 11.1 Data Link:<\/strong>\u00a0CQ 500 dataset<\/a><\/p>\n 11.2 Artificial Intelligence Project Idea:<\/strong>\u00a0Make a model for hospitals that can automatically generate a report of a fracture, bleeding or other things by analyzing the CT scan dataset.<\/p>\n The IMDB-Wiki dataset is one of the largest open-source datasets for face images with labeled gender and age. The images are collected from IMDB and Wikipedia. It has 5 million-plus labeled images.<\/p>\n 12.1 Data Link:<\/strong>\u00a0IMDB wiki dataset<\/a><\/p>\n 12.2 Artificial Intelligence Project Idea:<\/strong>\u00a0Make a model that will detect faces and predict their gender and age. You can have categories in different ranges like 0-10, 10-20, 30-40, 50-60, etc.<\/p>\n 12.3 Source Code:<\/strong> Gender & Age Detection Python Project<\/a><\/p>\n The dataset contains a CSV file that has 865 color names with their corresponding RGB(red, green and blue) values of the color. It also has the hexadecimal value of the color.<\/p>\n 13.1 Data Link: <\/strong>Color Detection Dataset\u00a0<\/a><\/p>\n 13.2 Artificial Intelligence Project Idea:\u00a0<\/strong>The color dataset can use used to make a color detection app in which we can have an interface to pick a color from the image and the app will display the name of the color.<\/p>\n 13.3 Source Code:<\/strong>\u00a0Color Detection Python Project<\/a><\/p>\n The youtube 8M dataset is a large scale labeled video dataset that has 6.1millions of Youtube video ids, 350,000 hours of video, 2.6 billion audio\/visual features, 3862 classes and 3avg labels per video. It is used for video classification purposes.<\/p>\n 1.1 Data Link:<\/strong>\u00a0Youtube 8M<\/a><\/p>\n 1.2 Machine Learning Project Idea:<\/strong>\u00a0Video classification can be done by using the dataset and the model can describe what video is about. A video takes a series of inputs to classify in which category the video belongs.<\/p>\n The urban sound dataset contains 8732 urban sounds from 10 classes like an air conditioner, dog bark, drilling, siren, street music, etc. The dataset is popular for urban sound classification problems.<\/p>\n 2.1 Data Link:<\/strong> Urban Sound 8K dataset<\/a><\/p>\n 2.2 Machine Learning Project Idea:<\/strong>\u00a0We can build a sound classification system to detect the type of urban sound playing in the background. This will help you get started with audio data and understand how to work with unstructured data.<\/p>\n Large scale scene understanding (LSUN) is a dataset of millions of colored images of scenes and objects. It is much bigger than imagenet dataset. There are around 59 million images, 10 different scenes categories, and 20 different object categories.<\/p>\n 3.1 Data Link:<\/strong>\u00a0LSUN dataset<\/a><\/p>\n 3.2 Machine Learning Project Idea:<\/strong>\u00a0Build a model to detect what scene is in the image. For example – a classroom, bridge, bedroom, curch_outdoor, etc. The goal of scene understanding is to gather as much knowledge of a given scene image as possible. It includes categorization, object detection, object segmentation.<\/p>\n RAVDESS is the acronym of The Ryerson Audio-Visual Database of Emotional Speech and Song. It contains audio files of 24 actors (12 male, 12 female ) with different emotions like calm, angry, sad, happy, fearful, etc. The expressions have two intensity normal and strong. The dataset is useful for speech emotion recognition.<\/p>\n 4.1 Data Link:<\/strong>\u00a0RAVDESS dataset<\/a><\/p>\n 4.2 Machine Learning Project Idea:<\/strong>\u00a0Build a speech emotion recognition classifier to detect the emotion of the speaker. The audio clips of people are classified into emotions like anger, happy, sad, etc.<\/p>\n2. Iris Dataset<\/h4>\n
3. MNIST Dataset<\/h4>\n
4. The Boston Housing Dataset<\/h4>\n
5. Fake News Detection Dataset<\/h4>\n
6. Wine quality dataset<\/h4>\n
7. SOCR data – Heights and Weights Dataset<\/h4>\n
8. Parkinson Dataset<\/h4>\n
9. Titanic Dataset<\/h4>\n
10. Uber Pickups Dataset<\/h4>\n
11. Chars74k Dataset<\/h4>\n
12. Credit Card Fraud Detection Dataset<\/h4>\n
14. AI generated Faces Dataset<\/h4>\n
Machine Learning Datasets for Natural Language Processing<\/h3>\n
<\/a><\/p>\n1. Enron Email Dataset<\/h4>\n
2. The Yelp Dataset<\/h4>\n
3. Jeopardy Dataset<\/h4>\n
4. Recommender Systems Dataset<\/h4>\n
5. UCI Spambase Dataset<\/h4>\n
6. Flickr 30k Dataset<\/h4>\n
7. IMDB reviews<\/h4>\n
8. MS COCO dataset<\/h4>\n
9. Flickr 8k Dataset<\/h4>\n
Machine Learning Datasets for Computer Vision and Image Processing<\/h3>\n
<\/a><\/p>\n1. CIFAR-10 and CIFAR-100 dataset<\/h4>\n
2. GTSRB (German traffic sign recognition benchmark) Dataset<\/h4>\n
3. ImageNet dataset<\/h4>\n
4. Breast Histopathology Images Dataset<\/h4>\n
5. Cityscapes Dataset<\/h4>\n
6. Kinetics Dataset<\/h4>\n
7. MPII human pose dataset<\/h4>\n
8. 20BN-something-something dataset v2<\/h4>\n
9. Object 365 Dataset<\/h4>\n
10. Photo sketching dataset<\/h4>\n
11. CQ500 Dataset<\/h4>\n
12. IMDB-Wiki dataset<\/h4>\n
13. Color Detection Dataset<\/h4>\n
Machine Learning Datasets for\u00a0Deep Learning<\/h3>\n
<\/a><\/p>\n1. Youtube 8M Dataset<\/h4>\n
2. Urban Sound 8K dataset<\/h4>\n
3. LSUN Dataset<\/h4>\n
4. RAVDESS Dataset<\/h4>\n