Path: tree/master/Machine Learning Supervised Methods
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Name | Size | Last Modified |
|---|---|---|
| 5. Use case on SVM.ipynb | 54.9 KB | |
| DAY 2 Understanding Simple Linear Regression .ipynb | 103.6 KB | |
| Day 2 Linear Regression using Python.ipynb | 694.3 KB | |
| Day 2 Linear Regression(multiple).ipynb | 430.1 KB | |
| Day 2 Use Case Linear Regression.ipynb | 656 KB | |
| Day1 ML Introduction treating a Data Set.ipynb | 1.7 MB | |
| Logistic Regression.ipynb | 70.6 KB | |
| Logistic_Regression on bank phone calls.ipynb | 209.7 KB | |
| Modelling Binary Logistic Regression Using Python.ipynb | 861.4 KB | |
| Personality.xlsx | 10.5 KB | |
| SVM for regression.ipynb | 488.9 KB | |
| SVM.ipynb | 739.6 KB | |
| Salary Prediction using Regression.ipynb | 108.9 KB | |
| Salary.xlsx | 674.8 KB | |
| Teledata.xlsx | 58 KB | |
| bill_authentication.csv | 46.4 KB | |
| car_price.csv | 1.9 MB | |
| diabetes.csv | 23.8 KB | |
| dummy | 1 bytes |