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Machine Learning And Data Science Project Practice Set- 1 | Hire Top Rated Machine Learning Expert



In this blog we will provide some important machine learning and data science problem set that can help you to improve your skills in machine learning. We are providing any type of machine learning and data science project help, assignment help and homework help services with an affordable prices.


Task 1

Pandas (for basic understanding) Practice


Questions

1. Please go through the 16 questions in the attached ipynb


2. Write well documented code to solve those questions and print proper output for all of them.


Note-1: If you have used code from somewhere, provide those references or citations. Else it will be considered plagiarism.

Note-2: Upload both python notebook and pdf version of that notebook.

Note-3: For converting Jupyter notebook to PDF please have a look at this

link:https://stackoverflow.com/questions/15998491/how-to-convert-ipython-notebooks-to-pdf-and-html.



Task 2

Python Practice Questions


Click here to download .ipynb notebook


1. There are a total of 10 questions in the attached ipynb file. (for the 10th question there is a type it should be 0.42)

2. The question is explained along with an example as well in the jupyter notebook itself.

3. Write well-documented code to solve those questions and print proper output for all of them.

4. Note that you are not supposed to use any libraries like numpy or scipy or similar libraries for this. You have to do this assignment completely from scratch in python and its functionalities.


This is a mandatory python assignment.

https://medium.com/@appliedaicourse_56208/faqs-of-python-mandatory-

assignment-7ada380ec770



Task 3

Exploratory Data Analysis on Haberman Dataset

Necessary files can be downloaded from the following links:




1. The data and reference notebook is attached here, try to document every plot and analysis that you do.

2. Experiment with different functionalities of jupyter notebook and get habituated with its features.

3. Try out as many plotting techniques as you can, but write down your observations for each of them.

4. Please be sure to have proper axes names, title and legend to all the charts that you plot.

5. Have a proper conclusions section where in you summarise your overall observation.

6. If you want to explore more about Haberman's Survival Data Set, you can try out this link https://www.kaggle.com/gilsousa/habermans-survival-data-set/version/1



Task 4

Implementing TFIDF vectorizer


Please check the video before working on the assignment

1. Please check the google drive link to download .ipynb files

2. We have given two ipython notebooks (1.Assignment_3_Reference.ipynb) in which we have implemented countvectorizer, (2.Assignment_3_Instructions.ipynb) you need to implement tfidf vectorizer, we have given the complete instructions in this notebook



Task 5

Implement RandomSearchCV with k fold cross validation on KNN


Please check the video before working on the assignment

1. Please check the google drive link to download .ipynb files

2. we have give two ipython notebooks (1.Assignment_4_Reference.ipynb) in which we have implemented GridsearchCV (2. Assignment_4_Instructions.ipynb) you need to implement


RandomsearchCV, we have given the complete instructions in this notebook



Task 6

Compute Performance metrics without Sklearn


Please check the video before working on the assignment


Please check the google drive link to download .ipynb files

Assignment instructions are mentioned in

6_Performance_metrics_Instructions.ipynb



Task 7

Apply Naive Bayes on Donors Choose dataset


Please check the video before working on the assignment


You can download the relevant files from the drive from here...



WRITE YOUR CODE IN THE SAME NOTEBOOKS



Task 8

Behavior of Linear Models


1. In this assignment, you will be solving 6 subproblems

2. Each subproblem will be having subtasks

3. Check the google drive folder to find out the instructions of each sub-problem: 

4. You can submit the assignment in 6 different notebooks or in a single note which will have all solutions to all the subproblems.



Task 9

Apply Decision Trees on Donors Choose dataset


Please check the video before working on the assignment


Necessary files can be downloaded from here




Task 10

1. Please check the google drive link to download .ipynb files

2. We have given two ipython notebooks (1.Central_Limit_theorem.ipynb) in which we have explained concepts of Confidence interval,(2. Bootstrap_Random_Forest_instructions.ipynb) you need to complete the

3 tasks that are given in this notebook




To get any help in above task or any other task you can contact us at:



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