Practical 11 : Write a program to implement OR, AND gate using Perceptron with learning rules.
Practical 4: Write a Python program to implement Simple Linear Regression
- How many total observations in data?
- How many independent variables?
- Which is a dependent variable?
- Quantify the goodness of your model and discuss steps taken for improvement (RMSE, SSE, R2Score).
Practical 5: Implementation of Multiple Linear Regression for House Price Prediction using sklearn.
Practical 6: Two Class Classification (Logistic Regression)
- How many total observations in data?
- How many independent variables?
- Which is a dependent variable?
- Implement logistic function.
- Implement Log-loss function.
- Quantify the goodness of your model and discuss steps taken for
improvement (Accuracy, Confusion matrices, F-measure).
Practical 7: Implementation of Decision tree using sklearn and its parameter tuning..
Practical 9 : Write a program to implement Random Forest Algorithm.
Practical 10 : Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file.
Practical 12 : Build an Artificial Neural Network by implementing the Backpropagation Algorithm.
Practical 3: Implement and demonstrate the FIND-S Algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file
Practical 8: Write a program to implement K-mean clustering in python.