Statistics Articles Links

1. All about p-value : https://www.analyticsvidhya.com/blog/2019/09/everything-know-about-p-value-from-scratch-data-science/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

2. Introduction to Probability : https://www.youtube.com/playlist?list=PLIeGtxpvyG-I9m4otjYGCQL_1m0Edm0LA

Data Science Articles Links

1. Extending churn analysis to revenue forecasting using R : https://www.datasciencecentral.com/profiles/blogs/extending-churn-analysis-to-revenue-forecasting-using-r

2. Exclusive Interview with Sonny Laskar – Kaggle Master and Analytics Vidhya Hackathon Expert: https://www.analyticsvidhya.com/blog/2019/05/exclusive-interview-sonny-laskar-kaggle-master-analytics-vidhya-hackathon-expert/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

3. A Comprehensive Guide to Data Exploration : https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/

4. Having an Imbalanced Dataset? Here Is How You Can Fix It. : https://towardsdatascience.com/having-an-imbalanced-dataset-here-is-how-you-can-solve-it-1640568947eb

5. Predictive Modeling: The Only Guide You'll Need : https://www.microstrategy.com/us/resources/introductory-guides/predictive-modeling-the-only-guide-you-need#customer

6. 1 Innovative Data Visualizations you Should Learn (in Python, R, Tableau and D3.js) : https://www.analyticsvidhya.com/blog/2019/08/11-data-visualizations-python-r-tableau-d3js/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

7. Rich with Visulization techniques: https://bbc.github.io/rcookbook/

8. Data Science Product : https://www.analyticsindiamag.com/13-leading-data-science-products-from-india-that-made-it-big-in-2019/

9. Handle missing Data in R codes : https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4

10. Introduction to Feature selaction _ Game Changer in Performance :- https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/

11. 5 Types of Regression : https://towardsdatascience.com/5-types-of-regression-and-their-properties-c5e1fa12d55e

Machine Learning Article Links

1. Using Linear Discriminant Analysis to Predict Customer Churn: https://www.datascience.com/blog/predicting-customer-churn-with-a-discriminant-analysis

2. Choosing the Correct Type of Regression Analysis: https://statisticsbyjim.com/regression/choosing-regression-analysis/

3. 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset: https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

4. Benchmarking 20 Machine Learning Models Accuracy and Speed: https://rpubs.com/m3cinc/Benchmarking_20_Machine_Learning_Models_Accuracy_and_Speed

5. Learn Artificial Intelligence with Machine Learning - 2019 : https://www.youtube.com/watch?v=RiC1BBKTqkA

6. 11 Important Model Evaluation Metrics for Machine Learning Everyone should know : https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

7. Comparing supervised learning algorithms : https://www.dataschool.io/comparing-supervised-learning-algorithms/

8. Do you know how to choose the right machine learning algorithm among 7 different types? : https://towardsdatascience.com/do-you-know-how-to-choose-the-right-machine-learning-algorithm-among-7-different-types-295d0b0c7f60

9. How to build Ensemble Models in machine learning? (with code in R) : https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/

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10. Fundamental Techniques of Feature Engineering for Machine Learning : https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114

11. Machine Learning Algorithms: Which One to Choose for Your Problem : https://blog.statsbot.co/machine-learning-algorithms-183cc73197c

12.  Deployed your Machine Learning Model? Here’s What you Need to Know About Post Production Monitoring : https://www.analyticsvidhya.com/blog/2019/10/deployed-machine-learning-model-post-production-monitoring/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

13.  Mathematics behind Machine Learning – The Core Concepts you Need to Know : https://www.analyticsvidhya.com/blog/2019/10/mathematics-behind-machine-learning/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

14. How to perform feature selection (i.e. pick important variables) using Boruta Package in R ?:  https://www.analyticsvidhya.com/blog/2016/03/select-important-variables-boruta-package/

15. Classification Accuracy is Not Enough: More Performance Measures You Can Use : https://machinelearningmastery.com/classification-accuracy-is-not-enough-more-performance-measures-you-can-use/

16. Precision vs Recall : https://towardsdatascience.com/precision-vs-recall-386cf9f89488

17. Check the comment on Cross Validation - Titaninc Study : https://www.reddit.com/r/kaggle/comments/dsf4gx/how_to_achieve_more_than_98_of_accuracy_on/