Complete-Life-Cycle-of-a-Data-Science-Project

CREDITS:All corresponding resources

MOTIVATION:Motivation to create this repository to help upcoming aspirants and help to others in the data science field

As data science is fastly developing field i found these few new techinques which make your work easier-https://github.com/achuthasubhash/Tips

Business understanding

1.Data collection

Data of 2 kinds

a.Structure data (tabular data,etc...)

b.Unstructured data (images,text,audio,etc...)

a.Web scraping best article to refer-https://towardsdatascience.com/choose-the-best-python-web-scraping-library-for-your-application-91a68bc81c4f

https://www.analyticsvidhya.com/blog/2019/10/web-scraping-hands-on-introduction-python/?utm_source=linkedin&utm_medium=KJ|link|weekend-blogs|blogs|44087|0.875

1.Beautifulsoup

2.Scrapy

3.Selenium

4.Request to access data 

5.AUTOSCRAPER - https://github.com/alirezamika/autoscraper

6.Twitter scraping tool (πšπš πš’πš—πš)-https://github.com/twintproject/twint

b.3rd party API'S

c.Big data engineering to collect data

d.Databases

Databases are 2 kind sequel and no sequel database

sql,mysql,mongodb,hadoop,elastic search,cassendra,amazon s3,hive,googlebigtable

e.Online resources - ultimate resource https://datasetsearch.research.google.com/

1)kaggle

2)movielens

3)data.gov:   https://data.gov.in/

4)uci

5)quandi

6)world3bank  https://data.world/

7)UCIMachineLearning

8)online hacktons

9)image data from Google_Search

10)image data from Bing_Search

11)https://www.columnfivemedia.com/100-best-free-data-sources-infographic

12)Reddit:https://lnkd.in/dv5UCD4

13)https://datasets.bifrost.ai/?ref=producthunt

14)data.world:https://lnkd.in/gEK897K

15)https://data.world/datasets/open-data

16)FiveThirtyEight :-  https://lnkd.in/gyh-HDj

17)BuzzFeed :- https://lnkd.in/gzPWyHj

18)Google public datasets :- https://lnkd.in/g5dH8qE

19)Quandl :- https://www.quandl.com

20)socorateopendata :- https://lnkd.in/gea7JMz

21)AcedemicTorrents :- https://lnkd.in/g-Ur9Xy

22)labelimage:- https://github.com/wkentaro/labelme  ,  https://github.com/tzutalin/labelImg

23)tensorflow_datasets as tfds

24)https://datasets.bifrost.ai/?ref=producthunt

25)https://ourworldindata.org/

26)https://data.worldbank.org/

27)google open images:https://storage.googleapis.com/openimages/web/download.html

28)https://data.gov.in/

29)imagenet dataset-http://www.image-net.org/

30)https://parulpandey.com/2020/08/09/getting-datasets-for-data-analysis-tasks%e2%80%8a-%e2%80%8aadvanced-google-search/

31)https://storage.googleapis.com/openimages/web/index.html  , 

   https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=segmentation&r=false&c=%2Fm%2F09qck
 
32)coco dataset https://cocodataset.org/#explore

33)huggingface datasets-https://github.com/huggingface/datasets

34)Big Bad NLP Database-https://datasets.quantumstat.com/

35)https://www.edureka.co/blog/25-best-free-datasets-machine-learning/

36)bigquery public datasets

2.Feature engineering

Data cleaning-Pyjanitor-https://analyticsindiamag.com/beginners-guide-to-pyjanitor-a-python-tool-for-data-cleaning/

Remove duplicate data in dataset

a.Handle missing value

 1.if missing data too small then delete it 
 
 2.replace mean(influenced by outiler),median(not influenced by outiler),mode
 
 3.apply classifier algorithm to predict missing value
 
 4.knn imputer
 
 5.apply unsupervised 
 
 6.Random Sample Imputation
 
 7.Adding a variable to capture NAN
 
 8.Arbitrary Value Imputation
 
 9.hot deck Imputation
 
 10.regression Imputation

b.Handle imbalance

 1.Under Sampling - mostly not prefer because lost of data
 
 2.Over Sampling  (RandomOverSampler (here new points create by same dot)) ,  SMOTETomek(new points create by nearest point so take long time)
 
 3.class_weight give more importance(weight) to that small class
 
 4.use kfold to keep the ratio of classess constant

c.Remove noise data

d.Format data

e.Handle categorical data Ordinal,Nominal,cyclic,binary categorical variables

 1.One Hot Encoding
 
 2.Count Or Frequency Encoding
 
 3.Target Guided Ordinal Encoding
 
 4.Mean Encoding
 
 5.Probability Ratio Encoding
 
 6.label encoding
 
 7.probability ratio encoding
 
 8.woe(Weight_of_evidence)
 
 9.one hot encoding with multi category (keep most frequently repeated only)
 
 10.feature hashing 
 
 11.sparse csr matrix
 
 12.entity embeddings
 
 13.binary encoding

f.Scaling of data

   1.Normalisation

   2.Standardization
 
   3.Min Max Scaling
 
   4.Robust Scaler not influenced by outliers because using of median,IQR
  
   5.Q-Q plot or Shapiro-Wilk Normality Test  is used to check whether feature is guassian or normal distributed  required for linear regression,logistic regression to 
     
      Improve performance
 
       a.Guassian Transformation
    
       b.Logarithmic Transformation
    
       c.Reciprocal Trnasformation
    
       d.Square Root Transformation
    
       e.Exponential Transdormation
    
       f.BoxCOx Transformation
    
       g.log(1+x) Transformation
       
       h.johnson

g.Remove low variance feature by using VarianceThreshold

h.Same variable in feature then remove feature

i.Outilers removing outilers depond on problem we are solving

  eg: incase of fraud detection outilers are very important
  
  methods to find outiler: zscore,boxplot,IQR

j.Sampling techniques

 a.biased sampling
 
 b.unbiased sampling

3.Exploratory Data Analysis(eda)

Explore the dataset by using  python or microsoft excel or tableau or powerbi, etc...

Data visualization (Matplotlib,Seaborn,Bokeh,etc...)

Scatterplot,line scatter plot,multi line plot,bubble chart,bar chart,histogram,boxplot,distplot,index plot,violin plotm,time series plot,density plot,dot plot,strip plot

data distributions-normal distribution,

4.Feature selection

1.pearson correleation

2.chisquare

3.Feature Importance

   a.ExtraTreesClassifier

   b.SelectKBest

   c.stepforward and stepbackward method

   d.Random_forest_importance

4.statics to select important feature (chi square  test,T test,anova test,hypothesis test)

5.keep in mind  curse of dimensionality

6.highly correleated then can remove 1 feature (multicollinearity)

7.dimension reduction

8.lasso regression to penalise unimportant features

9.filter method,wrapper method

10.threshold based method 

11.hypothesis testing

12.model based selection

13.Mutual Information Feature Selection

14.Correlation Feature Selection

15.remove features with very low variance

16.Univariate  feature selection

17.recursive feature  elimination

18.importance of feature (random forest importance)

19.feature importance with decision trees

20.forward elimination , backward elimination 

5.Model selection

Machine learning

A.Supervised learning (have label data)

 1.Regression (output feature in continous data form)
 
   linear regression,polynomial regression,support vector machine,Decision Tree Regression,Random Forest Regression,
   
   least square method,Random Forest Regression,xgboost,ridge(L2 Regularization),lasso(L1 Regularization),catboost,gradientboosting,adaboost,
   
   elsatic net,light gbm,ordinary least squares
   
   use cases:

 2.Classification (output feature in categorical data form)
 
    Logistic Regression,K-Nearest Neighbors,Support Vector Machine,Kernel SVM,Naive Bayes,Decision Tree Classification,
    
    Random Forest Classification,xgboost,adaboost,catboost,gaussian NB,LGBMClassifier,LinearDiscriminantAnalysis,
    
    passive aggressive classifier algorithm
    
    use cases:

B.Unsupervised learning(no label(target) data)

 1.Dimensionality reduction - PCA,SVD,LDA,tsne

 2.Clustering :https://scikit-learn.org/stable/modules/clustering.html

 3.Association Rule Learning - support,lift,confidence

 4.Recommendation system -
 
     a.collaborative Recommendation system,
     
     bcontent based Recommendation system 
     
     c.utility based Recommendation system 
     
     d.knowledge based Recommendation system 
     
     e.demographic based Recommendation system 
     
     f.hybrid based Recommendation system 

C.Ensemble methods

 1.Stacking models

 2.Bagging models

 3.Boosting models

D.Reinforcement learning

  agent apply action to environment get corresponding reward so that it learn environment
  
  1.Q-Learning
  
  2.Deep Q-Learning
  
  3.Deep Convolutional Q-Learning
  
  4.Twin Delayed DDPG
  
  5.A3C 

E.Deep-learning (use when have huge data and data is highly complex and state of art for unstructured data)

Frameworks:Pytorch,Tensorflow,Keras,caffe

1.Multilayer perceptron(MLP)

 1.Regression task

 2.Classification task

2.Convolutional neural network ( use for image data)

 1.Classification of image
 
   create own model,lenet,alexnet,resenet,inception,vgg,efficientnet,Nasnet
 
 2.Localization of object in image
 
 3.Object detection and object segmentation 
 
   rcnn,fastrcnn,fatercnn,yolo v1,yolo v2,yolo v3,yolo v4,fast yolo,yolo tiny,yolo lite,yolo tiny++,yolo act++,
   
   maskrcnn,ssd,detectron,detectron2,mobilenet,retinanet,R-fcn,detr facebook,U-net
   
   3 kind of object segmentation semantic segmentation,instance segmentation,panoptic segmentation
 
 4.Pose estimation 
 
 5.Deepdream,Neural style transfer

3.Recurrent neural network (use when series of data)

 1.RNN
 
 2.GRU
 
 3.LSTM (have memory cell,forget gate  etc..)
 
 all above 3 models have bidirectional also based on problem statement use bidirectional 

4.Generative adversarial network

5.Autoencoder

  1.sparse Autoencoder
  
  2.denoising Autoencoder
  
  3.Contractive Autoencoder
  
  4.stacked Autoencoder
  
  5.deep Autoencoder
  
  6.variational autoencoder

6.BoltzmannMachines,deep belief network,deep BoltzmannMachines

7.Self Organizing Maps (SOM) unsupervised learning

8.Natural language processing

 Clean data(removing stopwords depond on problem ,lowering data,tokenization,postagging,stemmimg or lemmatization depond on problem,skipgram,n-gram,chunking)
 
 Nltk,spacy,genism,textblob,inltk,stanza,polygot,corenlp,polyglot  libraries
 
 NLU,NLG,NER,text summarization,machine translation
  
 1.bag of words
 
 2.Tfidf
 
 3.using rnn,lstm,gru
 
 4.attention
 
 5.self attention
 
 6.wordembedding
    
    a.using pretrained model 
      
      i)word2vec( cbow,skipgram)
      
      ii)glove
      
      iiI)fasttext
    
    b.creating own embedding  (use when have huge data)
    
      i)word2vec library
      
      ii)keras embedding 
    
 7.Encoder and Decoder(sequence to sequence)
  
 8.Transformer (big breakthrough in NLP)
  
 9.BERT,ROBERTA,XLNet, XLM-RoBERTa, T5,DISTILBERT,GPT,GPT2,GPT3.

F.Time Series

  here data split is different (train,test,validate)
  
  here handling missing data different 
  
  generally used  to impute data in Time Series
  
  1.ffill
  
  2.bfill
  
  3.do mean of previous or future x samples and impute
  
  4.take previous year value and impute
  
  here model selection deponds on different property of data like stationary,trend,seasonality,cyclic
  
  adfuller test  for  Stationarity
  
  models 
  
  1.Arima , auto arima ,seasonal arima
  
  2.Autoregressive 
  
  3.Moving average 
  
  4.Lstm(neural network)
  
  5.Autoregressive
  
  6.Navie forecasts
  
  7.Smoothing (moving average,exponential smoothing)
  
  8.Facebook prophet (note:expceted date column as ds and target column as y)
  
  9.Holts winter,Holts linear trend
  
  10.AutoTS-https://analyticsindiamag.com/hands-on-guide-to-autots-effective-model-selection-for-multiple-time-series/
  
  11.Temporal Convolutional Neural
  
  12.Atspy For Automating The Time-Series Forecasting-https://analyticsindiamag.com/hands-on-guide-to-atspy-for-automating-the-time-series-forecasting/
  
  13.Darts-https://analyticsindiamag.com/hands-on-guide-to-darts-a-python-tool-for-time-series-forecasting/
  
  best article-https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/,
  
  https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
  
  https://github.com/Apress/hands-on-time-series-analylsis-python

G.Semi supervised learning,Self-Supervised Learning,Multi-Instance Learning

H.Active learning,Multi-Task Learning,Online Learning

I.Transfer learning

J.Deep dream,Style transfer

Hyperparameter tuning

a.GridSearchCV (check every given parameter so take long time)

b.RandomizedSearchCV (search randomly narrow down our time)

c.Bayesian Optimization

d.Sequential Model Based Optimization(Tuning a scikit-learn estimator with skopt)

e.Optuna

f.Genetic Algorithms 

g.Hyperopt

h.Keras tuner

Cross validation techniques- https://towardsdatascience.com/understanding-8-types-of-cross-validation-80c935a4976d

 1.Loocv
 
 2.Kfoldcv
 
 3.Stratfied cross validation
 
 4.Time Series cross-validation
 
 5.Holdout cross-validation
 
 6.Repeated cross-validation

Tensorboard to visualization of model performance

6.Testing model

Generally used metrics

 Always check bias variance tradeoff to know how model is performing
 
 Model can be overfitting(low bias,high variance),underfitting(high bias,high variance),good fit(low bias,low variance)
 
1.Regression task - mean-squared-error, Root-Mean-Squared-Error,mean-absolute error, RΒ², Adjusted RΒ²,Cross-entropy loss,Mean percentage error 

2.Classification task-Accuracy,confusion matrix,Precision,Recall,F1 Score,Binary Crossentropy,Categorical Crossentropy,AUC-ROC curve,log loss,Average precision,Mean average precision

3.Reinforcement learning - total rewards

4.Incase of machine translation use bleu score

5.Clustering then use silhouette score

6.Object Detection loss-localization loss,classification loss,Focal Loss,IOU,L2 loss,

If not giving good performance go back to Data collection or Feature engineering to increase performance of model

Docker and Kubernetes

7.deployment

1.Azure

2.Heroku

3.Amazon Web Services

4.Google cloud platform

Python Frameworks for App Development- Flask,Streamlit,Django,Web2py,Pyramid,CherryPy https://analyticsindiamag.com/top-8-python-tools-for-app-development/

Tensorflow lite:Use of tensorflow lite to reduce size of model

Quantization:Use Quantization to reduce size of model

8.Mointoring model

CI CD pipeline used- circleci , jenkins

BIG DATA: hadoop,apache spark

research paper-https://arxiv.org/ , https://www.kaggle.com/Cornell-University/arxiv

upcoming programming language for data science is julia

BEST ONLINE COURSES

1.COURSERA

2.UDEMY

3.EDX

4.DATACAMP

BEST YOUTUBE CHANNEL TO FOLLOW

1.Krish Naik-https://www.youtube.com/user/krishnaik06

2.Abhishek thakur-https://www.youtube.com/user/abhisheksvnit

3.AIEngineering-https://www.youtube.com/channel/UCwBs8TLOogwyGd0GxHCp-Dw

4.Ineuron-https://www.youtube.com/channel/UCb1GdqUqArXMQ3RS86lqqOw

5.Ken jee-https://www.youtube.com/c/KenJee1/featured

6.Codebasics-https://www.youtube.com/channel/UCh9nVJoWXmFb7sLApWGcLPQ           

7.3Blue1Brown-https://www.youtube.com/c/3blue1brown/featured

BEST BLOGS TO FOLLOW

1.Towards data science-https://towardsdatascience.com/

2.Analyticsvidhya-https://www.analyticsvidhya.com/blog/?utm_source=feed&utm_medium=navbar

3.Medium-https://medium.com/

4.Machinelearningmastery-https://machinelearningmastery.com/blog/

BEST RESOURCES

1.paperswithcode-https://paperswithcode.com/methods

2.madewithml-https://madewithml.com/topics/

3.Deep learning-https://course.fullstackdeeplearning.com/#course-content

4.pytorch deep learning-https://atcold.github.io/pytorch-Deep-Learning/

5.deep-learning-drizzle-https://deep-learning-drizzle.github.io/

6.Fastaibook-https://github.com/fastai/fastbook

7.TopDeepLearning-https://github.com/aymericdamien/TopDeepLearning

8.NLP-progress-https://github.com/sebastianruder/NLP-progress

9.EasyOCR-https://github.com/JaidedAI/EasyOCR

10.Awesome-pytorch-list-https://github.com/bharathgs/Awesome-pytorch-list

11.free-data-science-books-https://github.com/chaconnewu/free-data-science-books

12.arcgis-https://github.com/Esri/arcgis-python-api

13.data-science-ipython-notebooks-https://github.com/donnemartin/data-science-ipython-notebooks

14.julia-https://github.com/JuliaLang/julia , https://docs.julialang.org/en/v1/

15.google-research-https://github.com/google-research/google-research

16.reinforcement-learning-https://github.com/dennybritz/reinforcement-learning

17.keras-applications-https://github.com/keras-team/keras-applications , https://github.com/keras-team/keras

18.opencv-https://github.com/opencv/opencv

19.transformers-https://github.com/huggingface/transformers

20.code implementations for research papers-https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil

21.regarding satellite images-https://www.esri.com/en-us/arcgis/about-arcgis/overview

22.Monk_Object_Detection-https://github.com/Tessellate-Imaging/Monk_Object_Detection

23.NLP-progress - https://github.com/sebastianruder/NLP-progress

24.interview-question-data-science-https://github.com/iNeuronai/interview-question-data-science-

25.recommenders-https://github.com/microsoft/recommenders

26.Awesome-NLP-Resources -https://github.com/Robofied/Awesome-NLP-Resources

27.Tool for visualizing attention in the Transformer model-https://github.com/jessevig/bertviz

28.TransCoder-https://github.com/facebookresearch/TransCoder

29.Tessellate-Imaging-https://github.com/Tessellate-Imaging/monk_v1

Monk_Object_Detection-https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials- https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

30.Machine-Learning-with-Python-https://github.com/tirthajyoti/Machine-Learning-with-Python

31.huggingface-https://github.com/huggingface

32.multi-task-NLP-https://github.com/hellohaptik/multi-task-NLP

33.gpt-2 - https://github.com/openai/gpt-2

34.Powerful and efficient Computer Vision Annotation Tool (CVAT)-https://github.com/openvinotoolkit/cvat, https://github.com/abreheret/PixelAnnotationTool

https://github.com/UniversalDataTool/universal-data-tool

35.Data augmentation for NLP-https://github.com/makcedward/nlpaug

36.awesome Data Science-https://github.com/academic/awesome-datascience

37.mlops-https://github.com/visenger/awesome-mlops

38.gym-https://github.com/openai/gym

39.Super Duper NLP Repo-https://notebooks.quantumstat.com/

40.papers summarizing the advances in the field-https://github.com/eugeneyan/ml-surveys

41.deep-translator-https://github.com/nidhaloff/deep-translator

42.detext-https://github.com/linkedin/detext

43.nlpaug-https://github.com/makcedward/nlpaug

44.ipython-sql-https://github.com/catherinedevlin/ipython-sql

45.libra-https://github.com/Palashio/libra

46.opencv-https://github.com/opencv/opencv

47.learnopencv-https://github.com/spmallick/learnopencv , https://www.learnopencv.com/

48.math is fun-https://www.mathsisfun.com/ , https://pabloinsente.github.io/intro-linear-algebra, https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/

49.DEEP LEARNING WITH PYTORCH: A 60 MINUTE BLITZ - https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html

50.Spark Release 3.0.1-https://spark.apache.org/releases/spark-release-3-0-1.html

51.for more cheatsheets-https://github.com/FavioVazquez/ds-cheatsheets

52.text2emotion-https://pypi.org/project/text2emotion/

53.ExploriPy-https://analyticsindiamag.com/hands-on-tutorial-on-exploripy-effortless-target-based-eda-tool/

54.TCN-https://github.com/philipperemy/keras-tcn

55.deeplearning-models-https://github.com/rasbt/deeplearning-models

56.earthengine-py-notebooks-https://github.com/giswqs/earthengine-py-notebooks

57.NLP-progress -https://github.com/sebastianruder/NLP-progress

58.numerical-linear-algebra -https://github.com/fastai/numerical-linear-algebra

59.Super Duper NLP Repo- https://notebooks.quantumstat.com/

60.reinforcement learning by using PyTorch-https://github.com/SforAiDl/genrl

Follow leaders in the field to update yourself in the field

1.Linkedin

2.Twitter

Free CPU/GPU/TPU

1.Google cloab

2.Kaggle kernel

So what next ?

participate online competition and do project and apply to intership , job,real world problems, etc...

online competitions:

1.Kaggle-https://www.kaggle.com/

2.hackerearth-https://www.hackerearth.com/challenges/

3.machinehack-https://www.machinehack.com/

4.analyticsvidhya-https://datahack.analyticsvidhya.com/contest/all/

5.zindi-https://zindi.africa/competitions

6.crowdai-https://www.crowdai.org/

7.driven data-https://www.drivendata.org/

Some useful content :

  1. H20.ai

  2. Tpot

  3. autopandas

  4. AutoGluon

  5. autosklearn

  6. autoviml

  7. autoViz

  8. hyperopt

  9. sweetviz (EDA purpose) - https://pypi.org/project/sweetviz/

  10. pandasprofiling(display whole EDA) - https://pypi.org/project/pandas-profiling/

  11. autokeras

  12. pycaret- https://pycaret.org/

12.Auto_Timeseries by auto_ts

13.AutoNLP_Sentiment_Analysis by autoviml

14.automl lazypredict https://github.com/shankarpandala/lazypredict

15.bamboolib or pandas-ui (python package for easy data exploration & transformation) https://bamboolib.8080labs.com/ , https://pypi.org/project/pandas-ui/

16.CUPY (array process parallel in gpu) https://pypi.org/project/cupy/

17.Dabl has a built-in function that will automatically detect data types and quality issues and apply appropriate pre-processing to a dataset to prepare it for machine learning. https://pypi.org/project/dabl/

18.dask (parallel comptataion) https://docs.dask.org/en/latest/

19.dataprep (Understand your data with a few lines of code in seconds)

data-preparation-tools - https://improvado.io/blog/data-preparation-tools

20.Dora library is another data analysis library designed to simplify exploratory data analysis. https://pypi.org/project/Dora/

21.FastAPI is a modern, fast (high-performance), web framework for building APIs. https://fastapi.tiangolo.com/

22.faster Hyper Parameter Tuning(sklearn-nature-inspired-algorithms) https://pypi.org/project/sklearn-nature-inspired-algorithms/

23.FlashText (A library faster than Regular Expressions for NLP tasks) https://pypi.org/project/flashtext/

24.Guietta (tool that makes simple GUIs simple) https://pypi.org/project/guietta/

25.hummingbird (make code fastly exexcute) https://pypi.org/project/Hummingbird/

26.memory-profiler (tell memory consumption line by line) https://pypi.org/project/memory-profiler/

27.numexpr (incerease speed of execution of numpy) https://github.com/pydata/numexpr

28.pandarallel (simple and efficient tool to parallelize your pandas computation on all your CPUs) https://pypi.org/project/pandarallel/

29.PDFTableExtract(by PyPDF2) https://github.com/ashima/pdf-table-extract

30.PyImpuyte(Python package that simplifies the task of imputing missing values in big datasets) https://pypi.org/project/PyImpuyte/

31.libra(Automates the end-to-end machine learning process in just one line of code) https://pypi.org/project/libra/

32.debug code by puyton -m pdp -c continue

33.cURL (This is a useful tool for obtaining data from any server via a variety of protocols including HTTP.) https://stackabuse.com/using-curl-in-python-with-pycurl/

34.csvkit https://pypi.org/project/csvkit/

35.IPython IPython gives access to enhanced interactive python from the shell.

36.pip install faker (Create our own Dataset) https://pypi.org/project/Faker/

37.Python debugger %pdb

38.πšŸπš˜πš’πš•πšŠ-From notebooks to standalone web applications and dashboards https://voila.readthedocs.io/en/stable/ https://github.com/voila-dashboards/voila

39.πšπšœπš•πšŽπšŠπš›πš— for timeseries data https://github.com/tslearn-team/tslearn

40.texthero text-based dataset in Pandas Dataframe quickly and effortlessly https://github.com/jbesomi/texthero

41.πš”πšŠπš•πšŽπš’πšπš˜(web-based visualization libraries like your Jupyter Notebook with zero dependencies) https://pypi.org/project/kaleido/

42.Vaex- Reading And Processing Huge Datasets in seconds https://github.com/vaexio/vaex

43.Uber’s Ludwig is an Open Source Framework for Low-Code Machine Learning https://eng.uber.com/introducing-ludwig/

44.Google's TAPAS, a BERT-Based Model for Querying Tables Using Natural Language https://github.com/google-research/tapas

45.RAPIDS open GPU Data Science https://rapids.ai/

46.pyforest Lazy-import of all popular Python Data Science libraries. Stop writing the same imports over and over again. https://pypi.org/project/pyforest/0.1.1/

47.Modin Get faster Pandas with Modin https://github.com/modin-project/modin

48.Text2Code for Jupyter notebook - https://github.com/deepklarity/jupyter-text2code , https://towardsdatascience.com/data-analysis-made-easy-text2code-for-jupyter-notebook-5380e89bb493

49.Openrefine Tool-For Data Preprocessing Without Code https://analyticsindiamag.com/openrefine-tutorial-a-tool-for-data-preprocessing-without-code/

50.Microsoft Releases Latest Version Of DeepSpeed deep learning optimisation library known as DeepSpeed- https://github.com/microsoft/DeepSpeed

https://analyticsindiamag.com/microsoft-releases-latest-version-of-deepspeed-its-python-library-for-deep-learning-optimisation/

51.4-pandas-tricks-https://towardsdatascience.com/4-pandas-tricks-that-most-people-dont-know-86a70a007993

52.tkinter to deploy machine learning model-https://analyticsindiamag.com/complete-tutorial-on-tkinter-to-deploy-machine-learning-model/

53.autoplotter is a python package for GUI based exploratory data analysis-https://github.com/ersaurabhverma/autoplotter

54.3 NLP Interpretability Tools For Debugging Language Models-https://www.topbots.com/nlp-interpretability-tools/

I will be so happy that this repository helps you. Thank you for reading.

                                                    HAPPY LEARNING