Description: 

you will do basic python programming.  You will be provided with a dataset that describes the factors. Your task is to predict the values, with the provided features. You may find it necessary to drop irrelevant features, or to combine it.  

 

Instructions:

Google colab link for this assignment:
https://colab.research.google.com/drive/1dG06cjdI3P8LHE0AYfgBXkXY0q6ctOXL?usp=sharing
From the sklearn. Datasets - use any of the dataset (other than the digits)
Import pandas , matplotlib, sea born and other necessary libraries.
Take the snapshot of the executed results and paste it under  each question.
Submit the python file (.py or .ipynb )
Understand the machine learning concepts prior to the assignment work
Watch the broadcasted video on machine learning.
Avoid late submission - Check at the due date.
Assignments submitted after the due date are evaluated for 80%

 

 

Questions
Import the dataset, and examine the data

Use dir() function  on the dataset and find out the list of files
Find out the encoded labels for each data

Use the proper Matplotlib tool for visualization and plot the images
Using pandas, create the dataframe to manipulate the data and add a new column to the data frame, show it in the screen shot.

Use the proper sklearn function to create the training data and test data   , Use the Random forest classifier to predict the values of the model (use the minimum of 20 trees).

Plot the Confusion matrix to evaluate the accuracy of the classification. Show the graph to compare the predicted and actual values.