Data Science Projects in Python with Proper Project Description

1. LDA method Topic Modelling with RACE Dataset

The Project's goal is to find the dominating theme in the content or document. Words that are related logically and linguistically fall within the same theme. Large amounts of data can be labeled using topic modeling, and texts can be organized into themes and labels.

Project Descriptions and various segment of this project is explained

Dataset Pre-processing Steps

All words should be lowercase, tokenized, and lemmatized, and stop words and punctuation should be eliminated. The processed document is created by adding all of the document's tokens together. TFIDF, or a count vectorizer, is then used to transform the generated information.

Libraries in this Project include matpltlib, Numpy, nltk, sci-kit learn, pandas, and pvLDAvis tone. Some algorithms and approaches learned via this Python data science project include Latent Semantic Assessment, Linear Discriminant Allocation, and Non-negative Matrix Factorization.

Business Context

• Big data, deep learning, and artificial intelligence have made it urgently necessary to extract a single topic or a group of related topics from a document. Consider the situation where you must sort a plethora of documents into 10 - 20 categories and examine or review them. How monotonous and tedious will it be?
• Thanks to Language Models, each item can be categorized under a specific topic rather than manually going through many papers with Natural Reading Comprehension and Text Mining.
• Therefore, we anticipate that words from conceptually linked themes will appear together in a document more commonly than terms from unrelated ones.
• A topic is composed of a group of words that are regularly utilized in conjunction. For instance, it is more likely to come across words like "planet," "satellite," "cosm," "universe," and "asteroid" in a space-related paper. In contrast, phrases like habitat, species, mammal, plant, and landscape are more likely to be included in a text about wildlife. Words with similar attributes can be linked together using topic modeling, and different uses of languages with various meanings can be identified.
• A sentence or text contains many themes, each composed of several words.

Data Overview

The database comprises an odd 65000 papers with terms of many types, including nouns, adjectives, verbs, prepositions, and many others. Even the word count of documents varies greatly, with minimum word counts hovering around 40, and maximum word counts hovering around 500. 90% of the total data is used for training, while the remaining 10% is used to acquire a sense of how to forecast an issue upon documents that have not yet been viewed.

Objective

A dominant subject from each text is to be extracted or identified, followed by topic modeling.

Utilized resources and Libraries used in this Project

• Python will be a tool that we use to carry out a variety of tasks.
• Pandas is the main module utilized for information gathering and manipulation.
• Matplotlib with Bokeh for visualizing the structure of documents.
• NumPy for operations that require efficient computing.
• Topic modeling can be done using the Scikit Discover and Gensim packages, nltk for textual cleaning and preprocessing, and TSNE and pyLDAvis for topic visualization.

Approach

Topic EDA

• Word cloud's top terms within themes
• Distribution of topics using t-SNE
• Using an interactive tool, determine the topic distribution and word importance within topics pyLDAvis\s

Documents Pre-processing

• Reducing all word counts in papers and leaving only alphabets.
• Tokenizing every sentence, lemmatizing every word, and only saving in list words that are not stops and have a length of at least three alphabets.
• Adding your name to the list to create a document and maintain the lemmatized tokens for NMF Topic Modeling.
• Depending on the selected algorithm, transforming the aforementioned pre-processed data files using Count Vectorizer and TF IDF.
• Foreseeing a group of subjects and the main subject of each document.
• Using Command Prompt to perform a Python script form starting to conclusion

Topic Modelling algorithms

Latent Semantic Indexing (LSI), also known as Latent Semantic Analysis (LSA),

Non-Negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA), and the regularly utilized topic model metric factor called Coherence marks are examples of topic modeling techniques.

Code Overview

• The entire dataset is divided into 90% for training and 10% for predicting documents that have never been seen.
• Preprocessing is done to reduce noise by:
• Removing all words, replacing them in their original forms, and only keeping alphabets.
• After the tokenizing, process each of the sentences is lemmatized, creating a new document.
• TF IDF Vectorizer and Count vectorizer are transformed and fitted on a clean set of files for LSA and LDA topic modeling. Topics are extracted using the clean LSA and LDA packages, respectively, and ten topics were used for both algorithms.
• TF IDF Vectorizer is transformed and fitted on each token for NMF Topic Modeling. Thirteen topics were extracted, and the number was determined utilizing the Coherence marks.
• The t-SNE algorithm and the iterative tool pyLDAvis are used to examine the distribution of topics.
• The three algorithms mentioned above were used to predict topics for unseen documents.

2. LSTM Forecasting time series using long-short memory

Each node in the artificial recurrent network of neurons known as LSTM, or long-term memory network, has a memory cell. An LSTM differs from a feed-forward neural network because it contains receptive fields in its buried layers. It fixes the gradient's vanishing issue.

Sentiment classification, analysis, speech recognition, etc., are a few typical instances.

LSTM date Time - series data Prediction in Python Introduction

Deep learning is a rapidly developing topic in which we see many applications, such as segmentation, grouping, predicting, prognosis or recommendations, etc., in daily business operations. Due to the range of deep learning structures that academics and researchers have created, these intriguing applications are possible. The LSTM model is one of these time series forecasting models, and in this Project, we will focus on one specific kind of neural network method.

Project overview Forecasting Time Series Data Using LSTM in Python Recurrent artificial neural networks are one of the many varieties of deep learning architecture (RNN).

The Project will initially present more basic neural network techniques, such as perceptron, to help you comprehend various terms linked to neural networks because LSTM is a more advanced deep learning method. Following that, you will become familiar with several architectures for deep learning and employ LSTM for forecasting time series data.

Project Descriptions and various segment of this project is explained

Project Overview

This LSTM forecasting Python project will cover several exciting subjects.

Recurrent neural networks, deep belief networks, Convolutional neural networks, and Boltzmann networks are some of the well-known deep learning architectures covered in this Project. It will go over essential components like activation functions, perceptron elements, bias terms, and so on after introducing the fundamentals of these architectures. Understanding these aspects will assist you in comprehending the art of fine-tuning various deep-learning algorithms. It would also help us estimate how each deep learning algorithm differs from the others.

Additionally, the Project comes with a comprehensive installation guide, so you won't have to worry about it.

Collection Description

The Project's goal is to predict future passenger numbers for a given month based on historical data and recent memories. The data collection includes the monthly total of travelers who use a specific airline. The information is presented as follows: number of passengers, a month of the year.

Dataset

The data from an airline's passengers was the source for this LSTM-python project. Two columns are present: one column lists the calendar year and monthly to symbolize time, while the other lists the number of individuals who traveled during that month.

Data Normalization

The data is normalized using the MinMaxScaler method found in the preprocessing package under sklearn. Following the MinMaxScaler operation, the dataset must be converted underneath the 0 to 1 range.

Tech Stack requirements like Libraries in this Project: matpltlib, Numpy, nltk, sci-kit learn, pandas, pvLDAvis tone.

Data Pre-processing: You will learn how to use the Python library to normalize the variables in the dataset: The functions of sklearn, such as MinMaxScaler and StandardScaler. You will also be able to divide the dataset into test and training subsets and prepare it for using deep learning algorithms.

Time Series Forecasting with LSTM in Python

The Keras framework in Python lets users start from scratch with deep learning models. You will use Keras to create all of the layers of the LSTM-RNN model in this time series forecasting python project, and you will also make predictions about how many passengers will fly in the future. In addition, you will evaluate the model's accuracy using statistical tools.

3. Using Python's Multiclass Classification, recognize the human activity.

Fitness trackers and tablets that operate fitness monitoring applications can employ activity recognition. The project analyses the location, gyroscope, and accelerometer information to identify the movement of people, such as bicycling, strolling, lying down, and running. The Project is limited to 6 distinct tasks: Walking, lying down, moving up and down the stairs, sitting, and standing.

What is Human Activity Recognition?

• Estimating human poses is one of the most difficult aspects of activity recognition. This was previously accomplished by manually building a model that required careful setup and parameter estimation; However, using neural networks to estimate human poses is now simpler thanks to recent technological advancements.
• Experts in computer vision have been solving the problem of animal size estimation, which involves estimating an object's three-dimensional size from one image. Modeling the human body is the most important part of estimating human poses. Virtual characters can interact with users using human pose estimation and present things in the right alignment and position. The three most common animal/human body models utilized in animal/human pose estimation are the contour, skeleton, and volume-based models.
• IOT has made it easy for almost everyone to have a device that records their movements. It could be a pulsometer, a smartwatch, or even a smartphone.
• The features are typically extracted using a fixed-length sliding window method in this case. Any data, such as body acceleration, gravity acceleration, angular speed, etc., can be used. Identifying activity patterns and health monitoring are just a few of the many uses for human activity recognition.
• Human pose estimation has gained much popularity because of its practicality and adaptability. For instance, pose estimation can support research, enhance patient clinical cycle monitoring, and assist in determining posture labels with high accuracy in medical facilities.

Project Descriptions and various segment of this project is explained

Overview of the Using Python's Multiclass Classification, recognize the human activity.

As part of this project, we will create a system for categorizing human behaviors. The goal is to categorize various actions into one of the six tasks carried out while wearing a smartphone (in this case, a Samsung Galaxy S II) around the waist.

We recorded the 3-axial accelerometer, accelerometer, and gyroscope angular velocity at a consistent speed of 50Hz using its integrated accelerometer and gyroscope. The experiments were videotaped so that the data could be manually labeled. The obtained dataset was divided into two groups at random, with 30% of the volunteers chosen to create testing data and 70percent of the total number of participants chosen to generate training data.

Dataset Description

The information comes from a study in which 30 participants wore smartphones while engaging in various activities.

Data Preprocessing

• The mean, average, or null is used to replace any null values encountered during the data collection.
• The missing data is added to the data set using the mode. Mode substitution is the procedure.
• Keep track of each activity's frequency to see if the data favors one activity over another.
• A properly balanced dataset has approximately the same amounts of each activity's repetitions.

Exploratory Data Analysis

• Univariate analysis Each data variable in the data set is plotted against necessary fields like the standard deviation, mean, maximum, and minimum values. A bell-shaped normal distribution indicates that the data variable is evenly distributed throughout the data set.
• In bivariate analysis, the relationship between two distinct features on the y and x axes is shown. The relationships between features and variables can be better understood with the help of a graphical curve.
• tSNE plot When a large number of variables are involved, sometimes as many as 500, a multivariate analysis becomes challenging. No meaning to have 500 variables on a plot.
• When there are a lot of variables in the plot, tSNE plots make it easier to visualize multivariate systems in two dimensions.
• Normalization, also known as standardization, reduces the large variable ranges between -1 and 1. It measures each variable following a standard metric.
• The ideal output would be one with a mean of standard deviation and a zero of one after normalization.

Libraries - matplotlib, Python Pandas, seaborn, NumPy

Human Activity Recognition Image Dataset

The "Activity Recognition Using Mobile Smartphones Dataset," used in this machine learning human activity identification experiment, has been circulating since 2013. The data came from Thirty volunteers, aged between 19 and 48, who wore phones with inertial sensors put on their waists while performing one of six common activities: walking, climbing stairs, sitting, standing, or lying down. Each person videotaped the activities, and the movement data was manually extracted from these recordings. The UCI Machine-Learning Repository is where you may access this dataset without charge.

The Goal

The human activity recognition (HAR) project aims to classify a person's actions based on a variety of sensors' acquired parameters. To classify the activities of new, unseen subjects from their sensor data, Human Activity Recognition involves recording sensor data and related activities for specific subjects, fitting a model from this data, and generalizing the model.

Human activity classification has been a challenging task in computer vision for the past two decades. The field of behavior recognition has significant potential, as evidenced by previous research. The human activity recognition methods must first be divided into two main groups based on their sensor data: multimodal and unimodal approaches to activity recognition.

Depending on how they model human activities, these categories are further subdivided into space-time, stochastic, rule-based, and shape-based methods.

• Activity recognition methods, which represent human actions as a collection of spatiotemporal features or trajectories, are examples of space-time methods.
• Statistical models of human behavior, such as hidden Markov models, are used in stochastic approaches to identify activities.
• A set of rules is used in rule-based techniques to classify human actions.
• By simulating the movements of individual body parts, shape-based approaches effectively reflect high-level reasoning tasks.

Deep Learning Systems for Recognizing Human Activity

• Deep learning is a subset of machine learning and artificial intelligence (AI) that mimics human acquisition of particular information, like sensor data.
• It includes statistical analysis and predictive modeling, making it an essential part of data science.
• It speeds up and simplifies this procedure for data scientists who gather, evaluate, and comprehend huge amounts of data.
• It teaches a computer model how to use images, text, or sound to perform classification tasks.
• Because they use representation learning techniques that can automatically generate the best features from raw data, such as sensor data, without any human intervention and can discover hidden patterns in data, deep learning algorithms have recently become famous for recognizing human activity.
• The precision of deep learning models can often surpass that of humans.
• These models are trained with many labeled data and multiple-layer neural network architectures.
• Deep learning models are trained on large sets of labeled data with neural network architectures that learn features directly from the data without requiring manual feature extraction. "In contrast to the two to three hidden layers in conventional neural networks, these artificial neural networks can have as many as 150 hidden layers. Because they frequently use neural network architectures, methods for deep learning are referred to as "deep neural networks.

Different Classification Models for Deep Learning Activity Detection

Models

1. Convolutional Neural Networks (CNN)

Convolutional neural networks, also known as ConvNets, are among the deep neural networks that are utilized the most. A CNN is a great architecture for processing 2D data like images because it combines input data with trained features and uses 2D convolutional layers. CNN functions by directly extracting features from images. CNNs reduce the need for manual feature extraction, so you don't need to know which features are used to classify images. Instead of being pre-trained, the essential features are learned as the network is trained on a set of images. These models are extremely accurate for computer vision tasks like object classification thanks to automated feature extraction.

CNNs can learn to recognize various image features with the assistance of tens or hundreds of hidden layers. Depending on the structure of the object you are attempting to identify, the first hidden layer might learn to recognize edges, and the last hidden layer might learn to recognize more complex shapes. With each hidden layer, the figured-out image features become more complex.

For human activity recognition (HAR), convolutional neural network models are frequently utilized as a feature learning method. In contrast to traditional machine learning methods that require domain-specific knowledge, CNN can automatically extract features.

2. Deep neural networks

Deep recurrent neural networks, or RNNs, are a subset of neural networks designed to learn from data sequences, such as a series of time-lapsed observations or a sentence's word sequence. Image captioning, time-series analysis, natural language processing, handwriting recognition, and machine translation extensively use RNNs. Due to the directed cycles created by connections between recurrent neural network models, the LSTM outputs can be used as inputs in the current phase.

One type of recurrent neural network, the long short-term memory network (LSTM), is probably the most well-liked because of its meticulous design, which overcomes the common difficulties of training a stable RNN on sequence data. Over time, data is kept by LSTMs. They are useful for time-series prediction due to their recall of previous inputs. A chain-like structure is created by the four interacting LSTM layers' distinct interactions. Usually, LSTMs are used in areas like medical research, speech recognition, and time-series prediction.

What steps does machine learning take to recognize human activity?

It is still difficult for computer vision to detect human activity. The primary issues are the difficulty of activity detection and the number of people included in the analysis. At first, conventional approaches like Support Vector Machines and Hidden Markov Models attempted to comprehend the complexity of human pose estimation. Researchers later overcame the initial difficulties using the most recent advancements in machine learning and data mining.

The following are the steps for deep learning human activity recognition:

• Data Collection: Sensors collect data about how parts of the human body move.
• Pretreatment: The raw data is transformed into noise-free input by the deep learning algorithms, which then segment it to emphasize the analysis-relevant sections.
• Derivation of Features: Through a process called "feature extraction," the system finds relevant characteristics unique to a particular activity.
• Classification of Data: Processing and machine learning tools are used to classify the output.

Applications for Recognizing Human Influence using Computer Vision

• Sports Analytics The location of human joints can be tracked to monitor various sports performance indicators, which can greatly benefit athletes and coaches. Kinematic pose correction, for instance, can analyze athletes' performance and provide quantitative feedback during training. Motion analysis can also examine and train amateur athletes in various sports.
• Security and surveillance Video surveillance has taken on an enormous amount of significance for the sake of public safety. Governments frequently install CCTV cameras to monitor crowd behavior to maintain public safety. Even though cameras provide a lot of visual data, it still takes an intelligent and automatic system to spot violent or suspicious behavior. In this scenario, motion tracking may be beneficial.
• The purpose of the 3D pose estimation for human-computer interaction is to assist computers in acquiring a comprehensive comprehension of human behavior and actions. Using the pose estimation API, a robot that can recognize human actions, 3D poses, and emotions can quickly complete tasks. When a robot, for instance, detects the three-dimensional position of a person at risk of falling, it can respond appropriately. Additionally, assistant robots can engage in more social interactions with human users if they can recognize 3D human poses.

4. Using Keras and Tensor, Building a Similar Image Finder in Python

The Project aims to create a model that receives a photograph as input and outputs pictures similar to the patient's actual picture. By using this strategy, it displays more suggestions, which aids users in making informed decisions. Online retail platforms like Walmart, Alibaba, and others employ similar product recommendations based on product photos.

The project description and various segments of this project are explained below:

Business Objective

We are all conscious of how quickly e-commerce and the world wide web buying are expanding. Therefore, automatic and precise product recognition based on photographs at the inventory control unit (SKU) level is essential for computer vision systems. Meeting this market demand is the fundamental objective of our initiative. The primary objective of this task is to look for and locate product images comparable to any specified product image.

Tech Stack

Language: Python

Cloud support: AWS

Dataset Description

• Three columns make up the dataset: the public URL for each image, its unique identifier, and the class, which is used to categorize or describe the image.
• Elastic search is used for indexing, and MobileNetV2's ImageNet weights are utilized for feature extraction.
• Finding photographs most comparable to the image pixels is easier with the K closest neighbor technique. This is done for an image by locating the k-nearest components in the cluster map.

Libraries needed for the Project - Keras, Elastic search, Numpy, Tensorflow, Pandas, Sci-kit learn, and Requests are the libraries.

Data Overview

Images from enhancing the company's different product categories are included in the dataset, so each image has a ground truth class label. There are 90,834 photographs for testing, 10,095 images for validation, and 1,011,532 images overall.

It should be noted that only the Link is provided for each image. The users, on their initiative, must download the photos. It should be mentioned that the picture URLs can stop working eventually.

Approach

1. Obtain images from label id by utilizing the provided URLs to download every image.
2. ElasticSearch indexing with feature extraction using MobileNetV's imagenet weights.
3. K Nearest Neighbor in Elastic Search is used in the Image2Image Query to identify the K nearest vectors that share the greatest resemblance to the searched image.

5. Deep Learning Resume Parsing Using Python OCR and Spacy

Every month, thousands of resumes from job hopefuls arrive in the inboxes of recruiters and businesses. Sorting through thus many hiring processes for a person is quite difficult and stressful. The procedure quickly turns boring and mind-numbing. Resume parsing assists in organizing the crucial data in resumes into fundamental categories or labels. These labels serve as the key components of the resume's main idea. These labels may include a person's name, title, school, college, place of employment, etc. These resumes are processed into a form that only includes the most important data by a resume parser. Making the work of the recruiter more reasonable and less taxing.

The project description and various segments of this project are explained below:

Imagine that you were an intern in the Human Resources department of a company and were given a huge pile of approximately one thousand resumes. Your responsibility is to make a list of candidates who would be a good fit for the software engineer position. Now, because this company did not provide the candidates with a format for their resumes, it is your responsibility to examine each resume manually. What a drag, isn't it?

There is, however, an easy way out: developing a Resume Parsing Application that takes resumes as input and then extracts and analyzes all of the relevant data. It is difficult for recruiters and HR departments to sort through thousands of qualified resumes. They either lack qualified candidates or need many people to do this. A waste of a company's time, money, and productivity to manually separate candidates' resumes for excessive time. Therefore, we urge you to work on the Resume Parsing project, which has the potential to automate the separation process and save businesses a lot of time.

Dataset Description

The dataset is presented in JSON as (label, entity end tag, entity start tag, actual text)

As was already said, the categories that make up the core of the resume are labels. For example, name, title, city, experience, skills, etc. Processing of the dataset is required before modeling. Processing ensures that the data is structured correctly for use in Spacy NER.

Recognition Spacy Natural Entity

Python-based Identification is a framework for correlating text and its interpretation. It is a sophisticated natural language processing method that uses generative positional parsing. The method employs word embedding to reveal the relationship between a word's semantics and syntax.

For instance, "My time in Oxford was not enjoyable." After reviewing hundreds of applications with the same information or meanings, NER would realize that Cambridge refers to a college or school.

Using optical character recognition, text may be read from photographs and converted. The resumes are read using optical character recognition, turning them into text or pdf for use as model inputs.

Deep Learning Resume Parsing Using Python OCR and Spacy Project Objective

Tokenization, lemmatization, parts of speech tagging, and other NLP techniques like these are implemented in this Project utilizing the Python library SpaCy. For developing a Python resume parser. You will also learn to use optical character recognition (OCR) to extract textual data from the documents because all resumes are submitted in PDF format. The application will require minimal human intervention to extract crucial information from a resume, such as an applicant's name, work experience, and location. Try it because it is one of the most exciting NLP projects for beginners.

Our resume parser application can take in millions of resumes, parse the necessary fields, and classify them to solve this problem. SpaCy, a well-known Python library, is used for OCR and text classification in this resume parser. After using these fields to train our model, the application can identify their values from newly submitted resumes. Project

Here is an introduction to the exciting concepts you will learn when building a Python resume parser application system.

Process Tokenization

1. It is the procedure of dividing textual data into tokens or pieces.
2. A sentence can be broken up into tokens of words or characters, or The choice is based on the problem you want to solve.
3. It is typically the first step in any NLP project, and this resume parser made with an NLP project will follow suit.
4. Tokenization aids in the subsequent stages of an NLP pipeline, which typically involves assessing the weights of each word concerning the significance they hold in the corpus.

Process Lemmatization

The main objective of this Python resume parsing program is to decipher the text's semantics. For that, the verb's form does not significantly affect the sentence. Therefore, all words are lemmatized into their root form, referred to as a "lemma." For instance, the lemma "drive" is matched by the words "drive," "driven," and "drove."

Tagging Parts-of-Speech

The word "Apple" might signify two different things when used in a phrase. You can tell if someone is talking about the fruit of a large global computer business depending on whether the noun is used as a descriptive term or a common noun. This CV parser Python experiment will clarify how Python handles POS Tagging.

Process Stopwords Removal

Stopwords are words that barely add any sense to a sentence, such as "a," "the," "am," "is," etc. To conserve time and processor speed, these words are typically eliminated. Candidates may list their employment history in their CV in lengthy paragraphs with many stopwords.

SpaCy

1. SpaCy is a Python library that quickly implements the techniques mentioned above and is widely used by data scientists in numerous NLP-based projects.
2. SpaCy has a built-in visualizer called display that can be used to visualize various entities in text data.
3. SpaCy also makes it possible to use rule-based matching, shallow parsing, dependency parsing, and other similar techniques.
4. You will learn to use SpaCy for Named Entity Recognition (NER) in this NLP resume parser project.
5. OCR with TIKA In this Project, you will use Apache Tika, an open-source library, to implement OCR. The acronym "OCR" refers to optical character recognition. This resume extraction python project will be used to decode text information from PDF files by converting images into text.
6. Various NLP techniques are used to process the textual data and extract meaningful information.
7. Machine Learning Pipeline this Project uses machine learning and natural language processing to parse resumes. You will learn how a complete machine-learning project is used to solve real-world problems.
8. In this Project, neural networks built with the SpaCy library are used to build a model that can extract relevant fields like location, name, and so on. From various resumes in various formats.

Scaling Up Python's Resume Parser

The solution for a small dataset is laid out in this Project. However, you can refer to Model Deployment on GCP using Streamlit for Resume Parsing if you are interested in developing a Python model that is ready for production and can analyze millions of resume documents. Please remember that this model will need to be tagged and made to learn any new entities that may have been added before it can be used on many resumes.