Name	Name	Last commit message	Last commit date
Latest commit History 279 Commits
01. Day1 Python Decision Making	01. Day1 Python Decision Making
02. Day2 Python Data Structures	02. Day2 Python Data Structures
03. Day3 Python Function and Recursion	03. Day3 Python Function and Recursion
04. Day4 Python Classes and Objects	04. Day4 Python Classes and Objects
05. Day5 Python Modules	05. Day5 Python Modules
06. Day6 Numpy Basics	06. Day6 Numpy Basics
07. Day7 Numpy Random	07. Day7 Numpy Random
08. Day8 Pandas Tutorial	08. Day8 Pandas Tutorial
09. Day9 Pandas Data Manipulation	09. Day9 Pandas Data Manipulation
10. Day10 Pandas Data Cleaning	10. Day10 Pandas Data Cleaning
11. Day11 Matplotlib (Part - 1)	11. Day11 Matplotlib (Part - 1)
12. Day12 Matplotlib (Part - 2)	12. Day12 Matplotlib (Part - 2)
13. Day13 Matplotlib (Scatter & Pie Plot)	13. Day13 Matplotlib (Scatter & Pie Plot)
14. Day14 Seaborn (Part - 1)	14. Day14 Seaborn (Part - 1)
15. Day15 Seaborn (Part - 2)	15. Day15 Seaborn (Part - 2)
16. Day16 Python Revision	16. Day16 Python Revision
17. Day17 Numpy Revision	17. Day17 Numpy Revision
18. Day18 Pandas Revision	18. Day18 Pandas Revision
19. Day19 Matplotlib Revision	19. Day19 Matplotlib Revision
20. day20 Seaborn Revision	20. day20 Seaborn Revision
21. Day21 ML Basics	21. Day21 ML Basics
22. Day22 Supervised Learning	22. Day22 Supervised Learning
23. Day23 Unsupervised Learning	23. Day23 Unsupervised Learning
24. Day24 ML Workflow	24. Day24 ML Workflow
25. Day25 Model Evaluation	25. Day25 Model Evaluation
26. Day26 Underfitting and Overfitting	26. Day26 Underfitting and Overfitting
27. Day27 Cross-Validation	27. Day27 Cross-Validation
28. Day28 Training and Testing data	28. Day28 Training and Testing data
29. Day29 EDA Workflow	29. Day29 EDA Workflow
30. Day30 Simple basic EDA	30. Day30 Simple basic EDA
31. Day31 Linear Regression	31. Day31 Linear Regression
32. Day32 Multiple Linear Regression	32. Day32 Multiple Linear Regression
33. Day33 Linear Regression(Revision)	33. Day33 Linear Regression(Revision)
34. Day34 MLR Revision	34. Day34 MLR Revision
35. Day35 Classification	35. Day35 Classification
36. Day36 Logistic Regression	36. Day36 Logistic Regression
37. Day37 Logistic Regression(Iris)	37. Day37 Logistic Regression(Iris)
38. Day38 Logistic reg. Workflow	38. Day38 Logistic reg. Workflow
39. Day39 SVM Intro.	39. Day39 SVM Intro.
40. Day40 Linear SVM	40. Day40 Linear SVM
41. Day41 Non-Linear SVM	41. Day41 Non-Linear SVM
42. Day42 SVM Regression	42. Day42 SVM Regression
43. Day43 KNN Introduction	43. Day43 KNN Introduction
44. Day44 KNN Classification	44. Day44 KNN Classification
45. Day45 KNN Classification(IRIS)	45. Day45 KNN Classification(IRIS)
46. Day46 KNN Regression	46. Day46 KNN Regression
47. Day47 KNN Hyperparameter tuning	47. Day47 KNN Hyperparameter tuning
48. Decision Tree Concept	48. Decision Tree Concept
49. Day49 Decision Tree Implementation	49. Day49 Decision Tree Implementation
50. Day50 Decision Tree(Iris)	50. Day50 Decision Tree(Iris)
51. Day51 RandomForest Concept	51. Day51 RandomForest Concept
52. Day52 Random Forest Implementation	52. Day52 Random Forest Implementation
53. Day53 Random Forest(Iris)	53. Day53 Random Forest(Iris)
54. Day54 RF_Hyperparameter Tuning	54. Day54 RF_Hyperparameter Tuning
55. Day55 Ensemble Learning	55. Day55 Ensemble Learning
56. Day56 Naive Bayes	56. Day56 Naive Bayes
57. Day57 Intro. to Clustering	57. Day57 Intro. to Clustering
58. Day 58 K Means Concept	58. Day 58 K Means Concept
59. Day59 K Means Implementation	59. Day59 K Means Implementation
60. Day60 Hierarchical Clustering Concept	60. Day60 Hierarchical Clustering Concept
61. Day61 H-Clustering(Agglomerative Clustering)	61. Day61 H-Clustering(Agglomerative Clustering)
62. Day62 DBSCAN Concept	62. Day62 DBSCAN Concept
63. Day63 DBSCAN Implementation	63. Day63 DBSCAN Implementation
64. Day64 Dimensionality Reduction	64. Day64 Dimensionality Reduction
65. Day65 PCA Concept	65. Day65 PCA Concept
66. Day66 PCA Implementation	66. Day66 PCA Implementation
67. Day67 Feature Selection Intro.	67. Day67 Feature Selection Intro.
68. Day68 Feature Selection - Filter Method	68. Day68 Feature Selection - Filter Method
69. Day69 Feature Selection - Wrapper Method	69. Day69 Feature Selection - Wrapper Method
70. Day70 Feature Selection - Embedded Method	70. Day70 Feature Selection - Embedded Method
71. Day71 Intro. to Data Analytics	71. Day71 Intro. to Data Analytics
72. Day72 Intro. to Big Data	72. Day72 Intro. to Big Data
73. Day73 Intro. to Excel	73. Day73 Intro. to Excel
74. Day74 Intro. to Power BI	74. Day74 Intro. to Power BI
75. Day75 Simple BI Project	75. Day75 Simple BI Project
76. Day76 Intro. to Deep Learning	76. Day76 Intro. to Deep Learning
77. Day77 Intro. to Neural Networks	77. Day77 Intro. to Neural Networks
78. Day78 Intro. to Optimizers	78. Day78 Intro. to Optimizers
79. Day79 Intro. to NLP	79. Day79 Intro. to NLP
80. Day80 Intro. to Big Data	80. Day80 Intro. to Big Data
81. Day81 Intro. to Database	81. Day81 Intro. to Database
82. Day82 Intro. to SQL	82. Day82 Intro. to SQL
83. Day83 Overview of SQL	83. Day83 Overview of SQL
84. Day84 Webscrapping	84. Day84 Webscrapping
85. Day85 Webscrapping	85. Day85 Webscrapping
86-88 Day86-88 Heart Disease Prediction	86-88 Day86-88 Heart Disease Prediction
89-90 Day89-90 Loan Predictions with Comparing 3 models	89-90 Day89-90 Loan Predictions with Comparing 3 models
91-92 Day91-92 Drug Classification with various model	91-92 Day91-92 Drug Classification with various model
93-94 Day93-94 Diabetes Prediction with various model	93-94 Day93-94 Diabetes Prediction with various model
95-96 Day95-96 Mall Customer Segmentation	95-96 Day95-96 Mall Customer Segmentation
97-98 Day97-98 Flight Price Prediction using ML model	97-98 Day97-98 Flight Price Prediction using ML model
99-100 Day99-100 Car Evaluation Model	99-100 Day99-100 Car Evaluation Model
README.md	README.md

Name

Last commit message

Last commit date

01. Day1 Python Decision Making

02. Day2 Python Data Structures

03. Day3 Python Function and Recursion

04. Day4 Python Classes and Objects

05. Day5 Python Modules

06. Day6 Numpy Basics

07. Day7 Numpy Random

08. Day8 Pandas Tutorial

09. Day9 Pandas Data Manipulation

10. Day10 Pandas Data Cleaning

11. Day11 Matplotlib (Part - 1)

12. Day12 Matplotlib (Part - 2)

13. Day13 Matplotlib (Scatter & Pie Plot)

14. Day14 Seaborn (Part - 1)

15. Day15 Seaborn (Part - 2)

16. Day16 Python Revision

17. Day17 Numpy Revision

18. Day18 Pandas Revision

19. Day19 Matplotlib Revision

20. day20 Seaborn Revision

21. Day21 ML Basics

22. Day22 Supervised Learning

23. Day23 Unsupervised Learning

24. Day24 ML Workflow

25. Day25 Model Evaluation

26. Day26 Underfitting and Overfitting

27. Day27 Cross-Validation

28. Day28 Training and Testing data

29. Day29 EDA Workflow

30. Day30 Simple basic EDA

31. Day31 Linear Regression

32. Day32 Multiple Linear Regression

33. Day33 Linear Regression(Revision)

34. Day34 MLR Revision

35. Day35 Classification

36. Day36 Logistic Regression

37. Day37 Logistic Regression(Iris)

38. Day38 Logistic reg. Workflow

39. Day39 SVM Intro.

40. Day40 Linear SVM

41. Day41 Non-Linear SVM

42. Day42 SVM Regression

43. Day43 KNN Introduction

44. Day44 KNN Classification

45. Day45 KNN Classification(IRIS)

46. Day46 KNN Regression

47. Day47 KNN Hyperparameter tuning

48. Decision Tree Concept

49. Day49 Decision Tree Implementation

50. Day50 Decision Tree(Iris)

51. Day51 RandomForest Concept

52. Day52 Random Forest Implementation

53. Day53 Random Forest(Iris)

54. Day54 RF_Hyperparameter Tuning

55. Day55 Ensemble Learning

56. Day56 Naive Bayes

57. Day57 Intro. to Clustering

58. Day 58 K Means Concept

59. Day59 K Means Implementation

60. Day60 Hierarchical Clustering Concept

61. Day61 H-Clustering(Agglomerative Clustering)

62. Day62 DBSCAN Concept

63. Day63 DBSCAN Implementation

64. Day64 Dimensionality Reduction

65. Day65 PCA Concept

66. Day66 PCA Implementation

67. Day67 Feature Selection Intro.

68. Day68 Feature Selection - Filter Method

69. Day69 Feature Selection - Wrapper Method

70. Day70 Feature Selection - Embedded Method

71. Day71 Intro. to Data Analytics

72. Day72 Intro. to Big Data

73. Day73 Intro. to Excel

74. Day74 Intro. to Power BI

75. Day75 Simple BI Project

76. Day76 Intro. to Deep Learning

77. Day77 Intro. to Neural Networks

78. Day78 Intro. to Optimizers

79. Day79 Intro. to NLP

80. Day80 Intro. to Big Data

81. Day81 Intro. to Database

82. Day82 Intro. to SQL

83. Day83 Overview of SQL

84. Day84 Webscrapping

85. Day85 Webscrapping

86-88 Day86-88 Heart Disease Prediction

89-90 Day89-90 Loan Predictions with Comparing 3 models

91-92 Day91-92 Drug Classification with various model

93-94 Day93-94 Diabetes Prediction with various model

95-96 Day95-96 Mall Customer Segmentation

97-98 Day97-98 Flight Price Prediction using ML model

99-100 Day99-100 Car Evaluation Model

README.md

100-DaysOfCode-DataScience

About

Greetings! 👋 I'm Loga Aswin, diving into a 100-day data science immersion from Python fundamentals to real-world applications. This space will be a live documentation of my journey, where code meets curiosity. Let's connect, learn, and code together. Click ⭐ on GitHub to stay tuned for updates on my work!

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