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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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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
 
 

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100-DaysOfCode-DataScience

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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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