Artificial Intelligence/ Machine Learning/Deep Learning Practical Assignments

  Artificial Intelligence/ Machine Learning/Deep Learning 
Practical Assignments

1. Different ways to import any data set on Jupyter Notebook and store it into dataframe

2. Demonstration on commonly used ML libraries on any dataset 

3. Perform exploratory data analysis on SampleSuperstore data set

• Perform any 5 important operations and interpret the results
• Visualize the data using Pie chart, Bar Chart, Histogram, Distribution plot, countplot
• Interpret all results

4. Import any dataset and perform correlation matrix, covariance matrix. Interpret all results

5. Import any data set and generate a heatmap, interpret the results

6. Perform the ANOVA test and Interpret all results

7. Import News Paper dataset and apply Simple Linear Regression to predict sunday news paper circulation based on daily news paper circulation. Interpret all results

8. Import score dataset and apply simple linear regression to predict examination score. Interpret all results

9. Import Result Analysis dataset and apply Multilinear Regression to predict the examination result. Interpret all results

10. Import University Data set and perform clustering using KMeans Clustering techniques. Interpret all results

11. Import any dataset and perform data Standardization and Data normalization. Interpret all results

12. Import any dataset and perform LabelEncoding and OneHotEncoding techniques. Interpret all results

13. Import any Data set and perform Principle Component Analysis. Interpret all results

14. Import any dataset and perform text pre-processing and Feature Extraction

15. Import any dataset and perform Association mining.  Apriori technique and Associaton Rule technique

16. Import House Price data set and predict House Price.  using multilinear regression. Interpret all results

17. Import pima-indians-diabetes data set to classify patients as diabetic or non-diabetic suing SVM model. Interpret results

18. Import Iris dataset to classify Iris flowers into one of the three species (setosa, versicolor, or virginica) using a SVC , Support Vector Classifier and to evaluate the model's performance

19. Import car dataset and apply multilinear regression and predict the MPG

20. Import claimant's dataset and apply Logistic Regression and classify the claimants are eligible to hire the layer or not

21. Import wine dataset and perform EDA

22. Import any dataset and show the data splitting into x and y, training and testing datasets, train and test the model, and display evaluation of the model with confusion matrix

23. Import Isir dataset and perform basic EDA. Use labe encoding and Onehot encoding technique.
24. Import the cancer dataset and perform basic EDA
25. Use apple dataset and perform basic natural language preprocessing
26. Use Score dataset and show evaluation matrix such as MSE, R –Squared error
27. Use the logistic regression model on any dataset and show the model performance using a confusion matrix and classification report.
28. Use any dataset and perform data standardization and data normalization
29. Use and dataset and perform LabelEncoding and OneHot Encoding
30. Import the Iris dataset and display the total count of all three classes, display the count plot on species, use a pie chart to show the total count,