Top 20 Data Science and Machine Learning Projects in Python (Part-II)
Guys! I hope you all are cherished finding out my earlier article Part – I 10/20, and I perception that shall be useful for you. Let’s discuss in regards to the the rest of the enterprise shortly.
11. Learn to arrange information in your subsequent machine finding out enterprise.
Problem Statement & Solution
When you’re dealing with NLP based disadvantage assertion, we must always take care of “Text Data” preparation sooner than you may start using it for any NLP algorithm. The foremost step is that textual content material cleaning and processing is a crucial job in every machine finding out enterprise, even once we’re engaged on the text-based job and making sense of textual information. So, when dealing with textual content material, we must always take further causes for Text Classification, Text Summarization, understanding Tokenization, and Bag of Words preparation.
The large drawback proper right here is organising choices from Text Data and creating synthetic choices, which can be really important duties. On excessive of it the way in which to use machine finding out fashions to develop classifiers will be tough.
Indeed, this enterprise would help to know the textual content material classification and inclined analysis area in different domains.
Key take away and Outcome and of this enterprise.
- Understanding on
- NLTK library for NLP
- Stop phrases and use in the context of NLP
- The distinction between NLP, NLG, and NLU.
- TFIDF vector and its significance
- Text Specific Analytics
- Sentiment Analysis
- Text Classification
- Topic Modelling
- Text Summarization
- Tokenization and Bag of Words
- Part of Speech (POS) tagging
- Difference between Lemmatization and Stemming
- What is Binary Text classification and Text classification?
- How to make use of
- NLP pre-processing for teaching model
- Linear SVC for binary classification
- One Vs. Rest Classifier for Multi-Label Classification
- Multi-Label Binarizer for Multi-Label Classification
- Understanding the evaluation metrics
- Precision
- F1-score
- Recall
12. Time Series Forecasting with LSTM Neural Network Python
Problem Statement & Solution
Always Time assortment prediction points are a tough kind of predictive modelling points. The Long Short-Term Memory (LSTM) neighborhood is a type of recurrent neural neighborhood used in deep finding out since very large architectures may be effectively expert. It is used in order so as to add the complexity of sequences and dependence among the many many enter variables.
LSTM is a type of recurrent neural neighborhood that could be taught the dependence between devices in sequence order. This form itself ensures to have the power to understand the context required to make predictions in phrases of TIME-SERIES FORECASTING points.
As everyone knows, deep finding out is one major know-how the place we are going to implement many points as we do in our day-to-day enterprise operations, along with segmentation, clustering, forecasting, prediction, or suggestion, and so forth. Deep finding out construction has many branches. One of them is the recurrent neural neighborhood (RNN). The methodology that we’ll analyze in this deep-learning enterprise is about Long Short-Term Memory Network (LSTM) to hold out time assortment forecasting for univariate time assortment information.
Key take away and Outcome and of this enterprise.
- Understanding wanted libraries for making use of Neural Networks
- How to
- Install Keras and LSTM
- Perform EDA
- Plotting a Time Series plot
- Creating a Dataset matrix for making use of LSTM
- Understanding sequentially initializing a Neural Networks
- Defining the error carry out
- How to make use of LSTM as a training model
- Implementation of visualizing the loss and accuracy with each epoch
- How to tune the last word model and using it to make predictions.
13. Bosch Production Line Performance Data Science Project
Problem Statement & Solution
In this information science enterprise, we would predict inside failures of the assembly line in Bosch. The enterprise disadvantage has been addressed via the chocolate souffle manufacturing course of, and the strategy itself bit tough given that good chocolate souffle is decadent, delicious, and delicate. So, we’ve got now to adjust to up the steps; if one factor goes flawed, we must always retrace the steps we did flawed. But proper right here, we’ve got now the foremost profit with Bosch’s mechanical parts of the easiest top quality and safety necessities.
And on excessive of this, they’ve choices that file the data at every step alongside its assembly strains and can apply superior analytics to reinforce the manufacturing processes and its top quality. Even though the character of the data might vary, and its complexity would possibly enhance, we really need an data science-specific reply to resolve the problems relating to the manufacturing line dataset and predict inside failures using information accessible in each ingredient alongside the assembly line. This would facilitate Bosch to convey top quality merchandise at reducing costs to the end-user benefits.
Key take away and Outcome and of this enterprise.
- Understanding real-time enterprise context along with the Exploratory Data Analysis course of
- Handling null values, imbalanced, noisy, and most interesting evaluation metrics dataset.
- Applying
- Probabilistic model BernoulliNB for teaching
- Ensemble model
- Random Forest Classifier for teaching
- Extra Tree Classifier for teaching
- XGBoost Classifier for teaching
- Defining parameters for making use of
- GRID SEARCH CV
- Using
- Cross Folds Validation to cease overfitting
- Correlation and Violin plot for choosing the proper choices for the model
14. Classifying Handwritten Digits using MNIST Dataset
Problem Statement & Solution
Hand-written digit recognition is a tough job for the machine because of the hand-written digits won’t be always good and may be made with many various sides. The aim of this enterprise is to take the hand-written single-digit image and determine what that digit is. Please seek the advice of with the above picture(s).
Here we’re using the favored MNIST database of hand-written digits; the dataset consists of 60,000 pictures of 28×28 handwritten pixel digits and pre-processed and formatted. The model may be constructed to exactly be taught the handwritten digits with 95% accuracy using image recognition strategies and a suitable machine finding out algorithm. The charge of accuracy is decided by the chosen machine finding out algorithm.
Key take away and Outcome and of this enterprise.
- How to programmatically use with Python libraries
- Unzipping folders and loading the dataset
- Visualizing fully totally different pictures accessible in the dataset
- Plotting Confusion matrix and decoding the outcomes
- Predicting the top consequence and saving it in the kind of CSV
- Understanding
- Left-skew and Right-skew of the dataset
- Pre-processing the teaching dataset
- Ensemble model
- Random Forest
- MeanDecresedGini
- Hyper-parameter tuning Random Forest
- Training Neural Networks for predictions
- Plotting graphs in opposition to parameters and OOB errors
- How to be Importing
- FNN library and using Okay-nearest neighbours
- XGBoost and altering Dataset into DMatrix for performing predictions
- Defining parameters and performing Cross Folds validation using XGBoost model
15. Predict Employee Computer Access Needs in Python
Predicting employee entry should be based on their job place and obligations (R&R) from the employee database. What is the deal? I can understand your question. Yes. Of Course, this could be a giant deal like Amazon, Facebook, IBM and Microsoft, Google, Microsoft, and so forth.,
In this current digital world, when an employee begins his/her work, they should get a laptop computer laptop and entry to proprietary software program program wanted to fulfill their R&R. It is assumed that employees are fulfilling their capabilities of a given place on a day-to-day basis.
In some situations, in lots of the group, it is usually that the workers chase and work out the guidelines entry they need to accomplish their duties with the help of their counterparts, in another case as they encounter roadblocks all through their every day work a minimal of the preliminary interval.
This is type of attention-grabbing that automating the strategy based on the R&R. Since there is a considerable amount of information referring to an employee’s R&R generated inside an organization, really, we would use this information as a base; clearly, we must always revisit ceaselessly to large to the mapping, this mapping might be the helpful useful resource. We would possibly assemble the model that mechanically determines entry privileges and transfer on the information to the associated division and auto-installation course of. This might be very correctly related even employees enter and go away roles inside a company.
Ultimately, this enterprise would possibly assemble an auto-access machine finding out model that eliminates the considerable information intervention and retains the extraordinarily important half solely routed to information approvals. At the similar time, the model will revoke employee entry based on totally different situations.
Key take away and Outcome and of this enterprise.
- Understanding
- EDA and Visualization strategies
- Univariate Analysis and Data Transformation conversion
- Encoding and Decoding functionalities
- Okay-fold cross-validation
- Performing approximate greedy operate selection
- Applying
- Logistic Regression
- Hyper-parameter tuning
- Evaluation using AUC score
16. Forecasting Business KPI’s with TensorFlow and Python
As everybody is aware of that the very important promoting strategies are in trending branding, which helps corporations to develop. Branding helps the same old of the producers and stand out in a crowded enterprise market, So the intention is to realize the mannequin to targeted prospects in the market.
The companies make investments an amount in promoting their mannequin(s), and the return on funding will most likely be nothing nevertheless the number of product sales of their product(s). This is nothing nevertheless Return on funding (ROI).
In this ML enterprise, you will use the video clip of an IPL match carried out between two teams CSK Vs. RCB, the goal of this enterprise is to forecast only a few major KPIs – which is alleged to the number of cases that the mannequin emblem is in the frames, longest and shortest area share in the given video clip.
Key take away and Outcome and of this enterprise.
- Understanding the enterprise disadvantage.
- Learning
- How to remodel the XML data to CSV data.
- How to remodel the CSV data to tfrecords data.
- How to utilize the annotation software program (LabelImg) for producing XML data.
- What and the way in which to clone model.
- Visualization via the tensor boards.
- Generating frozen model from ckpts
- Calculating the numerous KPI metrics akin to
- The number of appearances of emblem
- The area, physique
- The shortest and largest area share.
- Understanding
- The concept of CKPT file in TensorFlow
- Making predictions using the expert model.
- How to course of the flicks and break them down into frames.
- How to course of the frames to realize predictions.
17. Time Series Python Project using Greykite and Neural Prophet
In this Machine Learning Project, we’re going to debate the TIME SERIES; Walmart’s sample information has been used to forecast product sales over time using the time assortment forecasting library known as Greykite, which helps us automate time series-based points.
As everyone knows that the time assortment is nothing nevertheless a sequence of information components which have been collected at a unbroken time interval, with These historic information components would help us to do the forecasting of the enterprise by making use of the statistical model by deriving the patterns out of it.
In the supply chain space, this time assortment having fun with a big place in different sides, in which DEMAND and SALES are important parts in enterprise; the goal of this enterprise is to take care of the second.
Key take away and Outcome and of this enterprise.
- Understanding
- Business context and aim and Inference of information
- Data Cleaning and Feature Engineering
- Time-series – parts,
- Trend and Seasonality Analysis
- Understanding Greykite library and establishing Greykite model
- Understanding Neural Prophet library and establishing Neural Prophet model
- Understanding
- AR web model
- Model Predictions
- Model Evaluation
- Flask Deployment
18. Deploying auto-reply Twitter take care of with Kafka, Spark, and LSTM
As everyone knows, pure language processing (NLP) focuses on decoding textual content material and speech in the similar technique as individuals do. It helps the computer to know by breaking down the human textual content material in methods in which make sense to absorbs and retailer the information. NLP’s place in social media is previous our thought course of, which helps to finds hidden information insights, notably in the home of Sentiment Analysis, in which by analyzing the languages used in social media critiques, suggestions as soon as extra the submit and extracts attitudes and emotions on the submit(s) – Eventually, this helps to promote the events and merchandise.
The companies have used this analysis for various features – campaigns of the product, purchaser mindset on the product. The full construction may be clubbed with NER (Named Entity Recognition), a way for recognizing phrases or sentences as treasured entities.
The aim of this auto-reply Twitter enterprise is to take heed to dwell tweets and reply once more using the NLP pipeline. The tweets will most likely be labeled based on the sentiment and categorised using Machine Learning fashions as LSTM using Flask and tweepy API and Big Data strategies which is deployed on AWS cloud suppliers.
Key take away and Outcome and of this enterprise.
- Understanding
- Exploring Tweepy API and dataset.
- Text classification and its features.
- NER (Named Entity Recognition) technique.
- LDA and information labelling.
- Spark and Kafka fundamentals
- Implementing
- Data cleaning and preparation course of for NLP duties using regex.
- Flask API and Kafka.
- Google Colab for teaching features.
19. Resume parsing with Machine finding out – NLP with Python OCR and Spacy
The enterprise is type of attention-grabbing and very fashionable thought. In this enterprise, the resume might be parsed and extract the Location/Designation/Name/Years of Experience/College/Degree/Graduation Year/Companies labored at/Email sort out put it once more into the dataset, by making HR’s resume selection course of straightforward and quicker means. This resume parser makes use of the favored Spacy library – for OCR (Optical character recognition) and Text classifications. Ultimately it saves time, money, and productiveness for the company by reducing the sturdy time of scanning a whole bunch of licensed resumes manually.
Key take away and Outcome and of this enterprise.
- Understanding
- Machine finding out framework
- Natural Language Processing
- OCR (Optical character recognition)
- Named Entity Recognition
- Annotations & Entities in Spacy
- Spacy Custom Model Building
- Incremental Spacy Model Building
- TIKA OCR course of
- How to Extract the textual content material from PDF
20. Create Your First Chatbot with RASA NLU Model and Python
A chatbot simulates and processing human dialog, it might presumably be each means [written or spoken]. This is laptop computer programming allowing individuals to work along with digital devices as an precise particular person interaction. We would possibly assemble two forms of chatbots – Rule-based chatbots and AI-based chatbots.
RULE-BASED CHATBOTS: Pre-defined tips, User enter ought to align with these pre-set tips to get an approximate reply.
AI-BASED CHATBOTS: Artificial intelligence (AI) based chatbots make use of Machine Learning algorithms to know the state of affairs and which implies of a question sooner than preparing a response once more to an interacting particular person. Answers are prepared by using Natural-Language responses. Based on the utilization these bots use to teach themselves and help questions
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Guys, Hope you had a superb experience by finding out all these initiatives and benefits, Will get once more to you with a singular topic shortly, see you shortly, Cheers! Shanthababu.