ML54Day

Day 1 Data Pre-processing

1. Importing Required Libraries
2. Importing Data
3. Handling Mising Data
4. Encoding Categorical Data
5. Splitting Data into train and test
6. Feature Scaling (standard scaler)

Day 2 Simple Linear Regression

1. Data Preprocessing
2. Fitting Simple Linear Regression Model to the training set
3. Predecting the Result
4. Visualization (matplotlib)

Day 3 Multiple Linear Regression

1. Data Preprocessing
2. Fitting Multiple Linear Regression to the Training set
3. Predicting the Test set results

Day 4 Logistic Regression

Linear v.s. Logistic regression

Day 5 Logistic Regression

Learned how cost function is calculated and then how to apply gradient descent algorithm to cost function to minimize the error in prediction.

Day 6 Implementing Logistic Regression

1. Data Pre-Processing
2. Logistic Regression Model
3. Predection
4. Evaluating The Predection