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01. Day 1 - Python Basics
01. Day 1 - Python Basics
 
 
02. Day 2 - Python Conditional & Loop
02. Day 2 - Python Conditional & Loop
 
 
03. Day 3 - Python Functions
03. Day 3 - Python Functions
 
 
04. Day 4 - Python Object Oriented
04. Day 4 - Python Object Oriented
 
 
05. Day 5 - Python Modules
05. Day 5 - Python Modules
 
 
06. Day 6 - Numpy
06. Day 6 - Numpy
 
 
07. Day 7 - Numpy Random
07. Day 7 - Numpy Random
 
 
08. Day 8 - Pandas Introduction
08. Day 8 - Pandas Introduction
 
 
09. Day 9 - Pandas _Analysis of housing data
09. Day 9 - Pandas _Analysis of housing data
 
 
10. Day 10 - Data Manipulation
10. Day 10 - Data Manipulation
 
 
11. Day 11 - Data Visualization with Matplotlib
11. Day 11 - Data Visualization with Matplotlib
 
 
12. Day 12 - Data Visualization with Seaborn
12. Day 12 - Data Visualization with Seaborn
 
 
13. Day 13 - Data Visualization with Pandas
13. Day 13 - Data Visualization with Pandas
 
 
14. Day 14 - Rename and Replace
14. Day 14 - Rename and Replace
 
 
15. Day 15 - Group by
15. Day 15 - Group by
 
 
16. Day 16 - Date, Categorical and Sparse Data
16. Day 16 - Date, Categorical and Sparse Data
 
 
17. Day 17 - Scaling
17. Day 17 - Scaling
 
 
18. Day – 18 Summary & Crosstab
18. Day – 18 Summary & Crosstab
 
 
19. Day 19 - EDA - Exploratory Data Analysis
19. Day 19 - EDA - Exploratory Data Analysis
 
 
20. Day 20 - Performance Metrics
20. Day 20 - Performance Metrics
 
 
21. Day 21 - Train & Test Split
21. Day 21 - Train & Test Split
 
 
22. Day 22 - Cross-Validation
22. Day 22 - Cross-Validation
 
 
23. Day 23 - Simple Linear Regression
23. Day 23 - Simple Linear Regression
 
 
24. Day 24 - Multiple Linear Regression
24. Day 24 - Multiple Linear Regression
 
 
25. Day 25 - Logistic Regression
25. Day 25 - Logistic Regression
 
 
26. Day 26 – SVM
26. Day 26 – SVM
 
 
27. Day 27 – KNN
27. Day 27 – KNN
 
 
28. Day 28 - Decision Tree
28. Day 28 - Decision Tree
 
 
29. Day 29 - Random Forest
29. Day 29 - Random Forest
 
 
30. Day 30 - Hyperparameter Tuning
30. Day 30 - Hyperparameter Tuning
 
 
31. Day 31 - Ensamble Learning
31. Day 31 - Ensamble Learning
 
 
32. Day 32 - Naive_Bayes
32. Day 32 - Naive_Bayes
 
 
33. Day 33 - K-Mean Clustering
33. Day 33 - K-Mean Clustering
 
 
34. Day 34 - DBSCAN Clustering
34. Day 34 - DBSCAN Clustering
 
 
35. Day 35 - Hierarchical Clustering
35. Day 35 - Hierarchical Clustering
 
 
36. Day 36 - Gaussian Mixture Model
36. Day 36 - Gaussian Mixture Model
 
 
README.md
README.md
 
 

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100-Days-of-Code-DataScience

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