No | Day | Date | Topic | link_category | Link | lag |
---|---|---|---|---|---|---|
1 | 2 | 10/11/19 | some errors in SQL 2/6 | github | https://github.com/viswanathanc/SQL-for-Data-Science | 1 |
2 | 14 | 10/23/19 | Matplolib – Visualization | kaggle | https://www.kaggle.com/viswanathanc/beginner-to-intermediate-matplotlib-visualizing | 12 |
3 | 22 | 10/31/19 | Role of EDA in Model Building | kaggle | https://www.kaggle.com/viswanathanc/role-of-eda-in-model-building | 19 |
4 | 27 | 11/05/19 | Beginner to intermediate matplotlib visualization | kaggle | https://www.kaggle.com/viswanathanc/beginner-to-intermediate-matplotlib-visualizing | 23 |
5 | 32 | 11/10/19 | bunch to dictionary | github | https://github.com/viswanathanc/basic_python | 27 |
6 | 33 | 11/11/19 | Stratified sampling | kaggle | https://www.kaggle.com/viswanathanc/stratifiedshufflesplit-working-with-less-data?scriptVersionId=23291002 | 27 |
7 | 33 | 11/11/19 | auto mpg | kaggle | https://www.kaggle.com/viswanathanc/auto-mpg-linear-regression?scriptVersionId=23291592 | 26 |
8 | 47 | 11/25/19 | Feature Selection Methods | github | https://github.com/viswanathanc/basic_python | 39 |
9 | 55 | 12/03/19 | Question by Question | kaggle | https://www.kaggle.com/viswanathanc/overview-of-datascience-2019 | 46 |
10 | 56 | 12/04/19 | Overview of data science 2019 | kaggle | https://www.kaggle.com/viswanathanc/overview-of-datascience-2019 | 46 |
11 | 57 | 12/05/19 | Pivoting SQL table | blog | https://viswa10.blogspot.com/2019/12/pivoting-sql-table.html | 46 |
12 | 62 | 12/10/19 | One Hot Encoding of Binary Variable | kaggle | https://www.kaggle.com/viswanathanc/ohe-of-binary-variable | 50 |
13 | 86 | 01/03/20 | P value | github | https://github.com/viswanathanc/statistics/blob/master/P%20value.ipynb | 73 |
14 | 97 | 01/14/20 | Enhancing the Python Codes | blog | https://viswa10.blogspot.com/2020/01/enhancing-python-codes.html?spref=tw | 83 |
15 | 99 | 01/16/20 | Visualizing Activation functions | github | https://github.com/viswanathanc/basic_python | 84 |
16 | 101 | 01/18/20 | Fun with datetime | github | https://github.com/viswanathanc/basic_python/blob/master/Fun%20with%20datetime.ipynb | 85 |
17 | 102 | 01/19/20 | One Hot Encoding of Binary Variable | kaggle | https://www.kaggle.com/viswanathanc/ohe-of-binary-variable?scriptVersionId=27230786 | 85 |
18 | 102 | 01/19/20 | Stock Market Prediction using Moving Average and Linear Regression | github | https://github.com/viswanathanc/time-series/blob/master/Stock%20Market%20analysis.ipynb | 84 |
19 | 103 | 01/20/20 | Forest Fire Regression Problem | github | https://github.com/viswanathanc/forest_fire/blob/master/EDA_Forest_Fire.ipynb | 84 |
20 | 104 | 01/21/20 | Why use CNN instead of normal Feed Forward Network? | blog | https://viswa10.blogspot.com/2020/01/why-use-cnn-instead-of-normal-feed.html?spref=tw | 84 |
21 | 105 | 01/22/20 | Plotting on map | kaggle | https://www.kaggle.com/viswanathanc/plotting-on-map/ | 84 |
22 | 105 | 01/22/20 | Stock Exchange Predictions | kaggle | https://www.kaggle.com/viswanathanc/time-series-stock-exchange-predictions | 83 |
23 | 107 | 01/24/20 | List of lists of Open Sources | kaggle | https://www.kaggle.com/general/127412 | 84 |
24 | 107 | 01/24/20 | List of lists of Open Sources Links | kaggle | https://www.kaggle.com/general/127412 | 83 |
25 | 108 | 01/25/20 | Time Series – Stock Price Predictions – Part 1 | kaggle | https://www.kaggle.com/viswanathanc/time-series-stock-price-predictions-part-1 | 83 |
26 | 108 | 01/25/20 | Introduction to Time Series Analysis -Part 2 | github | https://github.com/viswanathanc/time-series/blob/master/Introduction%20to%20Time%20Series%20Analysis%20-%202.ipynb | 82 |
27 | 109 | 01/26/20 | Time Series – Stock Price Predictions – Part 2 | kaggle | https://www.kaggle.com/viswanathanc/time-series-stock-price-predictions-part-2?scriptVersionId=27650049 | 82 |
28 | 111 | 01/28/20 | Knn regression with k=1 | kaggle | https://www.kaggle.com/general/127666#730506 | 83 |
29 | 111 | 01/28/20 | K Nearest Neighbor Algorithm | github | https://github.com/viswanathanc/Machine-Learning-Algorithms/blob/master/K%20Nearest%20Neighbors/K%20Nearest%20Neighbor%20Classification.ipynb | 82 |
30 | 113 | 01/30/20 | World Cities Dataset | kaggle | https://www.kaggle.com/viswanathanc/world-cities-datasets | 83 |
31 | 114 | 01/31/20 | Deep Learning (Goodfellow et al) - Chapter 1 review | blog | https://viswa10.blogspot.com/2020/01/deep-learning-goodfellow-et-al-chapter.html | 83 |
32 | 116 | 02/02/20 | Federated Learning – A solution to Data Privacy | kaggle | https://www.kaggle.com/general/128670 | 84 |
33 | 122 | 02/08/20 | Overview of data science 2019 | kaggle | https://www.kaggle.com/viswanathanc/overview-of-datascience-2019?scriptVersionId=28318605 | 89 |
34 | 122 | 02/08/20 | Code Signal – Almost Strictly Increasing | blog | https://viswa10.blogspot.com/2020/02/codesignal-almost-strictly-increasing.html | 88 |
35 | 123 | 02/09/20 | Chi Square test | github | https://github.com/viswanathanc/statistics/blob/master/Chi%20-%20Square%20test.ipynb | 88 |
36 | 127 | 02/13/20 | 'Module, package, framework and platform | blog | https://viswa10.blogspot.com/2020/02/module-package-framework-and-platform.html | 91 |
37 | 127 | 02/13/20 | Titanic Chi Square test - PClass vs Survied | github | https://github.com/viswanathanc/statistics/blob/master/Titanic%20Chi%20Square%20test%20-%20PClass%20vs%20Survied.ipynb | 90 |
38 | 129 | 02/15/20 | SMOTE – Ad’s success | kaggle | https://www.kaggle.com/viswanathanc/smote-ad-s-success | 91 |
39 | 129 | 02/15/20 | One Hot Encoding – question | kaggle | https://www.kaggle.com/questions-and-answers/130582#746503 | 90 |
40 | 130 | 02/16/20 | Some questions on Data Distribution | kaggle | https://www.kaggle.com/questions-and-answers/130481#747135 | 90 |
41 | 130 | 02/16/20 | Deep Learning (Goodfellow et al) - Chapter 2 review | blog | https://viswa10.blogspot.com/2020/02/deep-learning-goodfellow-et-al-chapter.html | 89 |
42 | 131 | 02/17/20 | Some Terminologies - Module, Package, Framework, API,.. | blog | https://viswa10.blogspot.com/2020/02/module-package-framework-and-platform.html | 89 |
I came across an interesting question in Code Signal. "Given a sequence of integers as an array, determine whether it is possible to obtain a strictly increasing sequence by removing no more than one element from the array." It is strictly increasing if every element in the array is greater than its successor. For a strictly increasing sequence we can check for each element whether it is greater than the next. In that case we can come to a conclusion that this sequence is not strictly increasing. If every element is greater than the successor we get to know it is a strictly increasing. For worst case(The sequence is strictly increasing), the algorithmic complexity is O(n). If we use similar approach for the question, we have to remove each element and pass the sequence to the above function. So for n elements, we use a fun
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